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1.
J Clin Monit Comput ; 2021 Nov 13.
Article in English | MEDLINE | ID: covidwho-1514056

ABSTRACT

The Hypotension Prediction Index (HPI) is a commercially available machine-learning algorithm that provides warnings for impending hypotension, based on real-time arterial waveform analysis. The HPI was developed with arterial waveform data of surgical and intensive care unit (ICU) patients, but has never been externally validated in the latter group. In this study, we evaluated diagnostic ability of the HPI with invasively collected arterial blood pressure data in 41 patients with COVID-19 admitted to the ICU for mechanical ventilation. Predictive ability was evaluated at HPI thresholds from 0 to 100, at incremental intervals of 5. After exceeding the studied threshold, the next 20 min were screened for positive (mean arterial pressure (MAP) < 65 mmHg for at least 1 min) or negative (absence of MAP < 65 mmHg for at least 1 min) events. Subsequently, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and time to event were determined for every threshold. Almost all patients (93%) experienced at least one hypotensive event. Median number of events was 21 [7-54] and time spent in hypotension was 114 min [20-303]. The optimal threshold was 90, with a sensitivity of 0.91 (95% confidence interval 0.81-0.98), specificity of 0.87 (0.81-0.92), PPV of 0.69 (0.61-0.77), NPV of 0.99 (0.97-1.00), and median time to event of 3.93 min (3.72-4.15). Discrimination ability of the HPI was excellent, with an area under the curve of 0.95 (0.93-0.97). This validation study shows that the HPI correctly predicts hypotension in mechanically ventilated COVID-19 patients in the ICU, and provides a basis for future studies to assess whether hypotension can be reduced in ICU patients using this algorithm.

2.
Ann Transl Med ; 9(9): 813, 2021 May.
Article in English | MEDLINE | ID: covidwho-1257379

ABSTRACT

BACKGROUND: Patients with coronavirus disease 2019 (COVID-19) may need hospitalization for supplemental oxygen, and some need intensive care unit (ICU) admission for escalation of care. Practice of adjunctive and supportive treatments remain uncertain and may vary widely between countries, within countries between hospitals, and possibly even within ICUs. We aim to investigate practice of adjunctive and supportive treatments, and their associations with outcome, in critically ill COVID-19 patients. METHODS: The 'PRactice of Adjunctive Treatments in Intensive Care Unit Patients with Coronavirus Disease 2019' (PRoAcT-COVID) study is a national, observational study to be undertaken in a large set of ICUs in The Netherlands. The PRoAcT-COVID includes consecutive ICU patients, admitted because of COVID-19 to one of the participating ICUs during a 3-month period. Daily follow-up lasts 28 days. The primary endpoint is a combination of adjunctive treatments, including types of oxygen support, ventilation, rescue therapies for hypoxemia refractory to supplementary oxygen or during invasive ventilation, other adjunctive and supportive treatments, and experimental therapies. We will also collect tracheostomy rate, duration of invasive ventilation and ventilator-free days and alive at day 28 (VFD-28), ICU and hospital length of stay, and the mortality rates in the ICU, hospital and at day 90. DISCUSSION: The PRoAcT-COVID study is an observational study combining high density treatment data with relevant clinical outcomes. Information on treatment practices, and their associations with outcomes in COVID-19 patients in highly and urgently needed. The results of the PRoAcT-COVID study will be rapidly available, and circulated through online presentations, such as webinars and electronic conferences, and publications in peer-reviewed journals-findings will also be presented at a dedicated website. At request, and after agreement of the PRoAcT-COVID steering committee, source data will be made available through local, regional and national anonymized datasets. TRIAL REGISTRATION: The PRoAcT-COVID study is registered at clinicaltrials.gov (study identifier NCT04719182).

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