Data consistency in the English Hospital Episodes Statistics database

BACKGROUND: To gain maximum insight from large administrative healthcare datasets it is important to understand their data quality. Although a gold standard against which to assess criterion validity rarely exists for such datasets, internal consistency can be evaluated. We aimed to identify inconsistencies in the recording of mandatory International Statistical Classification of Diseases and Related Health Problems, tenth revision (ICD-10) codes within the Hospital Episodes Statistics dataset in England. METHODS: Three exemplar medical conditions where recording is mandatory once diagnosed were chosen: autism, type II diabetes mellitus and Parkinson's disease dementia. We identified the first occurrence of the condition ICD-10 code for a patient during the period April 2013 to March 2021 and in subsequent hospital spells. We designed and trained random forest classifiers to identify variables strongly associated with recording inconsistencies. RESULTS: For autism, diabetes and Parkinson's disease dementia respectively, 43.7%, 8.6% and 31.2% of subsequent spells had inconsistencies. Coding inconsistencies were highly correlated with non-coding of an underlying condition, a change in hospital trust and greater time between the spell with the first coded diagnosis and the subsequent spell. For patients with diabetes or Parkinson's disease dementia, the code recording for spells without an overnight stay were found to have a higher rate of inconsistencies. CONCLUSIONS: Data inconsistencies are relatively common for the three conditions considered. Where these mandatory diagnoses are not recorded in administrative datasets, and where clinical decisions are made based on such data, there is potential for this to impact patient care

Variability in COVID-19 in-hospital mortality rates between national health service trusts and regions in England: A national observational study for the Getting It Right First Time Programme

Background A key first step in optimising COVID-19 patient outcomes during future case-surges is to learn from the experience within individual hospitals during the early stages of the pandemic. The aim of this study was to investigate the extent of variation in COVID-19 outcomes between National Health Service (NHS) hospital trusts and regions in England using data from March–July 2020. Methods This was a retrospective observational study using the Hospital Episode Statistics administrative dataset. Patients aged ≥ 18 years who had a diagnosis of COVID-19 during a hospital stay in England that was completed between March 1st and July 31st, 2020 were included. In-hospital mortality was the primary outcome of interest. In secondary analysis, critical care admission, length of stay and mortality within 30 days of discharge were also investigated. Multilevel logistic regression was used to adjust for covariates. Findings There were 86,356 patients with a confirmed diagnosis of COVID-19 included in the study, of whom 22,944 (26.6%) died in hospital with COVID-19 as the primary cause of death. After adjusting for covariates, the extent of the variation in-hospital mortality rates between hospital trusts and regions was relatively modest. Trusts with the largest baseline number of beds and a greater proportion of patients admitted to critical care had the lowest in-hospital mortality rates. Interpretation There is little evidence of clustering of deaths within hospital trusts. There may be opportunities to learn from the experience of individual trusts to help prepare hospitals for future case-surges

A novel ceramic coating for reduced metal ion release in metal‐on‐metal hip surgery

AbstractAn ovine total hip arthroplasty model was developed to evaluate metal ion release, wear, the biological response and adverse tissue reaction to metal‐on‐metal (MoM) bearing materials. The performance of an advanced superlattice ceramic coating (SLC) was evaluated as a bearing surface and experimental groups divided into; (1) MoM articulating surfaces coated with a SLC coating (SLC‐MoM), (2) uncoated MoM surfaces (MoM), and (3) metal on polyethylene (MoP) surfaces. Implants remained in vivo for 13 months and blood chromium (Cr) and cobalt (Co) metal ion levels were measured pre and postoperatively. Synovial tissue was graded using an ALVAL scoring system. When compared with the MoM group, sheep with SLC‐MoM implants showed significantly lower levels of chromium and cobalt metal ions within blood over the 13‐month period. Evidence of gray tissue staining was observed in the synovium of implants in the MOM group. A significantly lower ALVAL score was measured in the SLC‐MoM group (3.88) when compared with MoM components (6.67) (p = 0.010). ALVAL results showed no significant difference when SLC‐MOM components were compared to MoP (5.25). This model was able to distinguish wear and the effect of released debris between different bearing combinations and demonstrated the effect of a SLC coating when applied onto the bearing surface. © 2018 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater 107B: 1760–1771, 2019.</p

Frailty, comorbidity and associations with in-hospital mortality in older COVID-19 patients: an exploratory study of administrative data

BACKGROUND: Older adults have worse outcomes following hospitalisation with COVID-19, but within this group there is substantial variation. Although frailty and comorbidity are key determinants of mortality, it is less clear which specific manifestations of frailty and comorbidity are associated with the worst outcomes. OBJECTIVE: We aimed to identify the key comorbidities and domains of frailty that were associated with in-hospital mortality in older patients with COVID-19 using models developed using machine learning algorithms. METHODS: This was a retrospective study that used the Hospital Episode Statistics administrative dataset from 1st March 2020 to 28th February 2021 for hospital patients in England aged 65 years and over. The dataset was split into separate training (70%), test (15%) and validation (15%) datasets during model development. Global frailty was assessed using the Hospital Frailty Risk Score (HFRS) and specific domain of frailty identified using the Dr Foster Global Frailty Scale (GFS). Comorbidity was assessed using the Charlson Comorbidity Index (CCI). Additional features employed in the random forest algorithms included age, sex, deprivation, ethnicity, discharge month and year, geographical region, hospital trust, disease severity, International Statistical Classification of Disease and Related Health Problems 10th edition codes recorded during the admission. Features were selected, pre-processed and inputted into a series of random forest classification algorithms developed to identify factors strongly associated with in-hospital mortality. Two models were developed, the first model included the demographic, hospital-related and disease related items described above and individual GFS domains and CCI items. The second model was as the first but replaced the GFS domains and CCI items with the HFRS as a global measure of frailty. Model performance was assessed using the area under the receiver operating characteristic (AUROC) curve and measures of model accuracy. RESULTS: In total 215,831 patients were included. The model containing the individual GFS domains and CCI items had an AUROC curve for in-hospital mortality of 90% and a predictive accuracy of 83%. The model containing the HFRS had a similar performance (AUROC curve 90%, predictive accuracy 82%). The most important frailty items in the GFS were dementia/delirium, falls/fractures and pressure ulcers/weight loss. The most-important comorbidity items in the CCI were cancer, heart failure and renal disease. CONCLUSIONS: The physical manifestation of frailty and comorbidity, particularly a history of cognitive impairment and falls, may be useful in identification of patients who may need additional support during hospitalization with COVID-19