Factors associated with poorer outcomes for posterior lumbar decompression and or/or discectomy: an exploratory analysis of administrative data.

PURPOSE: This study aimed to identify factors associated with poorer patient outcomes for lumbar decompression and/or discectomy (PLDD). METHODS: We extracted data from the Hospital Episodes Statistics database for the 5 years from 1st April 2014 to 31st March 2019. Patients undergoing an elective one- or two-level PLDD aged ≥ 17 years and without evidence of revision surgery during the index stay were included. The primary patient outcome measure was readmission within 90 days post-discharge. RESULTS: Data for 93,813 PLDDs across 111 hospital trusts were analysed. For the primary outcome, greater age [< 40 years vs 70-79 years odds ratio (OR) 1.28 (95% confidence interval (CI) 1.14 to 1.42), < 40 years vs ≥ 80 years OR 2.01 (95% CI 1.76-2.30)], female sex [OR 1.09 (95% CI 1.02-1.16)], surgery over two spinal levels [OR 1.16 (95% CI 1.06-1.26)] and the comorbidities chronic pulmonary disease, connective tissue disease, liver disease, diabetes, hemi/paraplegia, renal disease and cancer were all associated with emergency readmission within 90 days. Other outcomes studied had a similar pattern of associations. CONCLUSIONS: A high-throughput PLDD pathway will not be suitable for all patients. Extra care should be taken for patients aged ≥ 70 years, females, patients undergoing surgery over two spinal levels and those with specific comorbidities or generalised frailty.

A statistical and machine learning approach to the study of astrochemistry.

In order to obtain a good understanding of astrochemistry, it is crucial to better understand the key parameters that govern grain-surface chemistry. For many chemical networks, these crucial parameters are the binding energies of the species. However, there exists much disagreement regarding these values in the literature. In this work, a Bayesian inference approach is taken to estimate these values. It is found that this is difficult to do in the absence of enough data. The Massive Optimised Parameter Estimation and Data (MOPED) compression algorithm is then used to help determine which species should be prioritised for future detections in order to better constrain the values of binding energies. Finally, an interpretable machine learning approach is taken in order to better understand the non-linear relationship between binding energies and the final abundances of specific species of interest.

Role of hospital strain in determining outcomes for people hospitalised with COVID-19 in England.

BACKGROUND: In England, reported COVID-19 mortality rates increased during winter 2020/21 relative to earlier summer and autumn months. This study aimed to examine the association between COVID-19-related hospital bed-strain during this time and patient outcomes. METHODS: This was a retrospective observational study using Hospital Episode Statistics data for England. All unique patients aged ≥18 years in England with a diagnosis of COVID-19 who had a completed (discharged alive or died in hospital) hospital stay with an admission date between 1 July 2020 and 28 February 2021 were included. Bed-strain was calculated as the number of beds occupied by patients with COVID-19 divided by the maximum COVID-19 bed occupancy during the study period. Bed-strain was categorised into quartiles for modelling. In-hospital mortality was the primary outcome of interest and length of stay a secondary outcome. RESULTS: There were 253 768 unique hospitalised patients with a diagnosis of COVID-19 during a hospital stay. Patient admissions peaked in January 2021 (n=89 047), although the crude mortality rate peaked slightly earlier in December 2020 (26.4%). After adjustment for covariates, the mortality rate in the lowest and highest quartile of bed-strain was 23.6% and 25.3%, respectively (OR 1.13, 95% CI 1.09 to 1.17). For the lowest and the highest quartile of bed-strain, adjusted mean length of stay was 13.2 days and 11.6 days, respectively in survivors and was 16.5 days and 12.6 days, respectively in patients who died in hospital. CONCLUSIONS: High levels of bed-strain were associated with higher in-hospital mortality rates, although the effect was relatively modest and may not fully explain increased mortality rates during winter 2020/21 compared with earlier months. Shorter hospital stay during periods of greater strain may partly reflect changes in patient management over time.

Data quality and autism: Issues and potential impacts.

INTRODUCTION: Large healthcare datasets can provide insight that has the potential to improve outcomes for patients. However, it is important to understand the strengths and limitations of such datasets so that the insights they provide are accurate and useful. The aim of this study was to identify data inconsistencies within the Hospital Episodes Statistics (HES) dataset for autistic patients and assess potential biases introduced through these inconsistencies and their impact on patient outcomes. The study can only identify inconsistencies in recording of autism diagnosis and not whether the inclusion or exclusion of the autism diagnosis is the error. METHODS: Data were extracted from the HES database for the period 1st April 2013 to 31st March 2021 for patients with a diagnosis of autism. First spells in hospital during the study period were identified for each patient and these were linked to any subsequent spell in hospital for the same patient. Data inconsistencies were recorded where autism was not recorded as a diagnosis in a subsequent spell. Features associated with data inconsistencies were identified using a random forest classifiers and regression modelling. RESULTS: Data were available for 172,324 unique patients who had been recorded as having an autism diagnosis on first admission. In total, 43.7 % of subsequent spells were found to have inconsistencies. The features most strongly associated with inconsistencies included greater age, greater deprivation, longer time since the first spell, change in provider, shorter length of stay, being female and a change in the main specialty description. The random forest algorithm had an area under the receiver operating characteristic curve of 0.864 (95 % CI [0.862 - 0.866]) in predicting a data inconsistency. For patients who died in hospital, inconsistencies in their final spell were significantly associated with being 80 years and over, being female, greater deprivation and use of a palliative care code in the death spell. CONCLUSIONS: Data inconsistencies in the HES database were relatively common in autistic patients and were associated a number of patient and hospital admission characteristics. Such inconsistencies have the potential to distort our understanding of service use in key demographic groups.

Frailty, Comorbidity, and Associations With In-Hospital Mortality in Older COVID-19 Patients: Exploratory Study of Administrative Data.

BACKGROUND: Older adults have worse outcomes following hospitalization 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 for machine learning algorithms. METHODS: This was a retrospective study that used the Hospital Episode Statistics administrative data set from March 1, 2020, to February 28, 2021, for hospitalized patients in England aged 65 years or older. The data set was split into separate training (70%), test (15%), and validation (15%) data sets during model development. Global frailty was assessed using the Hospital Frailty Risk Score (HFRS) and specific domains of frailty were identified using the 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, and International Statistical Classification of Disease, 10th Edition codes recorded during the admission. Features were selected, preprocessed, and input 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, as well as individual GFS domains and CCI items. The second model was similar to 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 using 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 using the HFRS had 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 manifestations of frailty and comorbidity, particularly a history of cognitive impairment and falls, may be useful in identification of patients who need additional support during hospitalization with COVID-19.

Vaccine-Induced Subcutaneous Granulomas in Goats Reflect Differences in Host-Mycobacterium Interactions between BCG- and Recombinant BCG-Derivative Vaccines.

Tuberculous granulomas are highly dynamic structures reflecting the complex host-mycobacterium interactions. The objective of this study was to compare granuloma development at the site of vaccination with BCG and its recombinant derivatives in goats. To characterize the host response, epithelioid cells, multinucleated giant cells (MNGC), T cell subsets, B cells, plasma cells, dendritic cells and mycobacterial antigen were labelled by immunohistochemistry, and lipids and acid-fast bacteria (AFB) were labelled by specific staining. Granulomas with central caseous necrosis developed at the injection site of most goats though lesion size and extent of necrosis differed between vaccine strains. CD4+ T and B cells were more scarce and CD8+ cells were more numerous in granulomas induced by recombinant derivatives compared to their parental BCG strain. Further, the numbers of MNGCs and cells with lipid bodies were markedly lower in groups administered with recombinant BCG strains. Microscopic detection of AFB and mycobacterial antigen was rather frequent in the area of central necrosis, however, the isolation of bacteria in culture was rarely successful. In summary, BCG and its recombinant derivatives induced reproducibly subcutaneous caseous granulomas in goats that can be easily monitored and surgically removed for further studies. The granulomas reflected the genetic modifications of the recombinant BCG-derivatives and are therefore suitable models to compare reactions to different mycobacteria or TB vaccines.

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.

Evaluation of Twitter data for an emerging crisis: an application to the first wave of COVID-19 in the UK.

In the absence of nationwide mass testing for an emerging health crisis, alternative approaches could provide necessary information efficiently to aid policy makers and health bodies when dealing with a pandemic. The following work presents a methodology by which Twitter data surrounding the first wave of the COVID-19 pandemic in the UK is harvested and analysed using two main approaches. The first is an investigation into localized outbreak predictions by developing a prototype early-warning system using the distribution of total tweet volume. The temporal lag between the rises in the number of COVID-19 related tweets and officially reported deaths by Public Health England (PHE) is observed to be 6-27 days for various UK cities which matches the temporal lag values found in the literature. To better understand the topics of discussion and attitudes of people surrounding the pandemic, the second approach is an in-depth behavioural analysis assessing the public opinion and response to government policies such as the introduction of face-coverings. Using topic modelling, nine distinct topics are identified within the corpus of COVID-19 tweets, of which the themes ranged from retail to government bodies. Sentiment analysis on a subset of mask related tweets revealed sentiment spikes corresponding to major news and announcements. A Named Entity Recognition (NER) algorithm is trained and applied in a semi-supervised manner to recognise tweets containing location keywords within the unlabelled corpus and achieved a precision of 81.6%. Overall, these approaches allowed extraction of temporal trends relating to PHE case numbers, popular locations in relation to the use of face-coverings, and attitudes towards face-coverings, vaccines and the national 'Test and Trace' scheme.