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OBJECTIVES BACKGROUND: To externally validate clinical prediction models that aim to predict progression to invasive ventilation or death on the ICU in patients admitted with confirmed COVID-19 pneumonitis. DESIGN: Single-center retrospective external validation study. DATA SOURCES: Routinely collected healthcare data in the ICU electronic patient record. Curated data recorded for each ICU admission for the purposes of the U.K. Intensive Care National Audit and Research Centre (ICNARC). SETTING: The ICU at Manchester Royal Infirmary, Manchester, United Kingdom. PATIENTS: Three hundred forty-nine patients admitted to ICU with confirmed COVID-19 Pneumonitis, older than 18 years, from March 1, 2020, to February 28, 2022. Three hundred two met the inclusion criteria for at least one model. Fifty-five of the 349 patients were admitted before the widespread adoption of dexamethasone for the treatment of severe COVID-19 (pre-dexamethasone patients). OUTCOMES: Ability to be externally validated, discriminate, and calibrate. METHODS: Articles meeting the inclusion criteria were identified, and those that gave sufficient details on predictors used and methods to generate predictions were tested in our cohort of patients, which matched the original publications' inclusion/exclusion criteria and endpoint. RESULTS: Thirteen clinical prediction articles were identified. There was insufficient information available to validate models in five of the articles; a further three contained predictors that were not routinely measured in our ICU cohort and were not validated; three had performance that was substantially lower than previously published (range C-statistic = 0.483-0.605 in pre-dexamethasone patients and C = 0.494-0.564 among all patients). One model retained its discriminative ability in our cohort compared with previously published results (C = 0.672 and 0.686), and one retained performance among pre-dexamethasone patients but was poor in all patients (C = 0.793 and 0.596). One model could be calibrated but with poor performance. CONCLUSIONS: Our findings, albeit from a single center, suggest that the published performance of COVID-19 prediction models may not be replicated when translated to other institutions. In light of this, we would encourage bedside intensivists to reflect on the role of clinical prediction models in their own clinical decision-making.
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Following Sir Bruce Keogh's review of 14 NHS Trusts, Buckinghamshire NHS Trust was found to have higher mortality rates than the England average. As part of a series of implementations and investigations to address this, a quality improvement project looking at clinical responses to the deteriorating patients was designed. Buckinghamshire NHS Trust utilises the National Early Warning Score (NEWS) metric for observations and escalation, and this was the standard used for the project. Episodes were eligible for inclusion if the NEWS score was increased to 5 or above. Data was collected by junior doctors from acute wards across the trust using notes and charts available. The initial cycle identified that in 57% of cases the high NEWS was escalated for review. Only half of cases were reviewed by a doctor; only a third were reviewed within an hour. In only 20% of cases were all criteria of the NEWS guidelines met. The first intervention was through education. After this, the project was completed on a monthly basis for 6 months with additional interventions introduced, including increased medical staff availability, grand round presentations, and increased outreach provision. Over this 6 month period, there was an increase to 87% of cases being reviewed by a doctor of appropriate seniority. Whilst this is a surrogate for reducing mortality and improving the clinical care given in the hospital, these results suggest successful interventions for improving clinical response to deteriorating patients across the trust. The project has recruited a new cohort of juniors to continue the quality improvement cycle.