Background Troponin elevation is common in hospitalized COVID-19 patients, but underlying aetiologies are ill-defined. We used multi-parametric cardiovascular magnetic resonance (CMR) to assess myocardial injury in recovered COVID-19 patients. Methods and results One hundred and forty-eight patients (64 ± 12 years, 70% male) with severe COVID-19 infection [all requiring hospital admission, 48 (32%) requiring ventilatory support] and troponin elevation discharged from six hospitals underwent convalescent CMR (including adenosine stress perfusion if indicated) at median 68 days. Left ventricular (LV) function was normal in 89% (ejection fraction 67% ± 11%). Late gadolinium enhancement and/or ischaemia was found in 54% (80/148). This comprised myocarditis-like scar in 26% (39/148), infarction and/or ischaemia in 22% (32/148) and dual pathology in 6% (9/148). Myocarditis-like injury was limited to three or less myocardial segments in 88% (35/40) of cases with no associated LV dysfunction; of these, 30% had active myocarditis. Myocardial infarction was found in 19% (28/148) and inducible ischaemia in 26% (20/76) of those undergoing stress perfusion (including 7 with both infarction and ischaemia). Of patients with ischaemic injury pattern, 66% (27/41) had no past history of coronary disease. There was no evidence of diffuse fibrosis or oedema in the remote myocardium (T1: COVID-19 patients 1033 ± 41 ms vs. matched controls 1028 ± 35 ms; T2: COVID-19 46 ± 3 ms vs. matched controls 47 ± 3 ms). Conclusions During convalescence after severe COVID-19 infection with troponin elevation, myocarditis-like injury can be encountered, with limited extent and minimal functional consequence. In a proportion of patients, there is evidence of possible ongoing localized inflammation. A quarter of patients had ischaemic heart disease, of which two-thirds had no previous history. Whether these observed findings represent pre-existing clinically silent disease or de novo COVID-19-related changes remain undetermined. Diffuse oedema or fibrosis was not detected.

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Objectives: To compare risks of hypernatraemia on admission to hospital in persons who were with those who were not identified as care home residents and evaluate the association of hypernatraemia with in-hospital mortality. Design: Retrospective observational study. Setting: A National Health Service Trust in London. Participants: A total of 21,610 patients aged over 65 years whose first admission to the Trust was between 1 January 2011 and 31 December 2013. Main outcome measures: Hypernatraemia on admission (plasma Na > 145 mmol/L) and in-hospital death. Results: Patients admitted from care homes had 10-fold higher prevalence of hypernatraemia than those from their own homes (12.0% versus 1.3%, respectively; odds ratio [OR]: 10.5, 95% confidence interval [CI]: 8.43-13.0). Of those with hypernatraemia, nine in 10 cases were associated with nursing home ECOHOST residency (attributable fraction exposure: 90.5%), and the population attributable fraction of hypernatraemia on admission associated with care homes was 36.0%. After correcting for age, gender, mode of admission and dementia, care home residents were significantly more likely to be admitted with hypernatraemia than were own-home residents (adjusted odds ratio [AOR]: 5.32, 95% CI: 3.85-7.37). Compared with own-home residents, care home residents were also at about a two-fold higher risk of in-hospital mortality compared with non-care home residents (AOR: 1.97, 95% CI: 1.59-2.45). Consistent with evidence that hypernatraemia is implicated in higher mortality, the association of nursing homes with in-hospital mortality was attenuated after adjustment for it (AOR: 1.61, 95% CI: 1.26-2.06). Conclusions: Patients admitted to hospital from care homes are commonly dehydrated on admission and, as a result, appear to experience significantly greater risks of inhospital mortality.

Objective Clinical interventions and death in the intensive care unit (ICU) depend on complex patterns in patients’ longitudinal data. We aim to anticipate these events earlier and more consistently so that staff can consider preemptive action. Materials and Methods We use a temporal convolutional network to encode longitudinal data and a feedforward neural network to encode demographic data from 4713 ICU admissions in 2014–2018. For each hour of each admission, we predict events in the subsequent 1–6 hours. We compare performance with other models including a recurrent neural network. Results Our model performed similarly to the recurrent neural network for some events and outperformed it for others. This performance increase was more evident in a sensitivity analysis where the prediction timeframe was varied. Average positive predictive value (95% CI) was 0.786 (0.781–0.790) and 0.738 (0.732–0.743) for up- and down-titrating FiO2, 0.574 (0.519–0.625) for extubation, 0.139 (0.117–0.162) for intubation, 0.533 (0.492–0.572) for starting noradrenaline, 0.441 (0.433–0.448) for fluid challenge, and 0.315 (0.282–0.352) for death. Discussion Events were better predicted where their important determinants were captured in structured electronic health data, and where they occurred in homogeneous circumstances. We produce partial dependence plots that show our model learns clinically-plausible associations between its inputs and predictions. Conclusion Temporal convolutional networks improve prediction of clinical events when used to represent longitudinal ICU data.