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.

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.