In March 2020, the World Health Organisation declared the COVID-19 outbreak a pandemic. Multiple new guidelines were proposed and existing models of social, domestic and hospital care altered. Most healthcare systems were largely unprepared for this and this pandemic has tested their adaptability. This study is aimed at assessing the impact of covid-19 on the demographics, presentation and clinical management of patients with proximal femoral (hip) fractures.

This retrospective multi-centre cohort study compared all patients admitted with hip fractures, between 1^st March and 30th May 2019 (Group PC: Pre-Covid) with hip fracture patients admitted over the same time period during the pandemic in 2020 (Group C: Covid). The data was obtained from the hospitals' local and National Hip Fracture Databases. Mortality data was checked with the Office for National Statistics (ONS). Primary outcomes were time to theatre, in-patient length of stay and 30-day mortality.

580 patients were included (304 PC, 276 C). Patient Charlson comorbidity index and Nottingham Hip Fracture scores were broadly similar across the two cohorts. There was a significant reduction in percentage of total hip replacements in Group C (11% to 5%, p=0.006). There was an increase in conservative management in group C (1% to 5%, p=0.002). The time to theatre was significantly delayed in Group C (43.7 hours C versus 34.6 hours PC, p<0.001). Overall length of hospital stay was similar in both groups (16.6 days PC versus 15 days C, p=0.089). 30-day mortality rate in Group C was 9.8% compared to 8.2% in Group PC (p=0.431), but for covid (+) patients it was significantly higher at 38.2% versus 5.8% in covid (−) patients (p<0.001).

This is one of the largest multi-centre comparative cohort study in the literature to date, examining the impact of the covid-19 pandemic on the management of hip fracture patients. Whilst mortality rates were similar in both groups, covid patients were almost seven times more likely to die, reflecting the seriousness of the covid-19 infection and its sequelae in such elderly, vulnerable patients.

INTRODUCTION

Quality monitoring is increasingly important to support and assure sustainability of the Orthopaedic practice. Many surgeons in a non-academic setting lack the resources to accurately monitor quality of care. Widespread use of electronic medical records (EMR) provides easier access to medical information and facilitates its analysis. However, manual review of EMRs is inefficient and costly. Artificial Intelligence (AI) software has allowed for development of automated search algorithms for extracting relevant complications from EMRs. We questioned whether an AI supported algorithm could be used to provide accurate feedback on the quality of care following Total Hip Arthroplasty (THA) in a high-volume, non-academic setting.