Application of machine learning approach in emergency department to support clinical decision making for SARS-CoV-2 infected patients.

To support physicians in clinical decision process on patients affected by Coronavirus Disease 2019 (COVID-19) in areas with a low vaccination rate, we devised and evaluated the performances of several machine learning (ML) classifiers fed with readily available clinical and laboratory data. Our observational retrospective study collected data from a cohort of 779 COVID-19 patients presenting to three hospitals of the Lazio-Abruzzo area (Italy). Based on a different selection of clinical and respiratory (ROX index and PaO2/FiO2 ratio) variables, we devised an AI-driven tool to predict safe discharge from ED, disease severity and mortality during hospitalization. To predict safe discharge our best classifier is an RF integrated with ROX index that reached AUC of 0.96. To predict disease severity the best classifier was an RF integrated with ROX index that reached an AUC of 0.91. For mortality prediction the best classifier was an RF integrated with ROX index, that reached an AUC of 0.91. The results obtained thanks to our algorithms are consistent with the scientific literature an accomplish significant performances to forecast safe discharge from ED and severe clinical course of COVID-19.

PaO2/FiO2 ratio forecasts COVID-19 patients' outcome regardless of age: a cross-sectional, monocentric study.

We studied the predictive value of the PaO2/FiO2 ratio for classifying COVID-19-positive patients who will develop severe clinical outcomes. One hundred fifty patients were recruited and categorized into two distinct populations ("A" and "B"), according to the indications given by the World Health Organization. Patients belonging the population "A" presented with mild disease not requiring oxygen support, whereas population "B" presented with a severe disease needing oxygen support. The AUC curve of PaO2/FiO2 in the discovery cohort was 0.838 (95% CI 0.771-0.908). The optimal cut-off value for distinguishing population "A" from the "B" one, calculated by Youden's index, with sensitivity of 71.79% and specificity 85.25%, LR+4.866, LR-0.339, was < 274 mmHg. The AUC in the validation cohort of 170 patients overlapped the previous one, i.e., 0.826 (95% CI 0.760-0.891). PaO2/FiO2 ratio < 274 mmHg was a good predictive index test to forecast the development of a severe respiratory failure in SARS-CoV-2-infected patients. Moreover, our work highlights that PaO2/FiO2 ratio, compared to inflammatory scores (hs-CRP, NLR, PLR and LDH) indicated to be useful in clinical managements, results to be the most reliable parameter to identify patients who require closer respiratory monitoring and more aggressive supportive therapies. Clinical trial registration: Prognostic Score in COVID-19, prot. NCT04780373 https://clinicaltrials.gov/ct2/show/NCT04780373 (retrospectively registered).

The identification and management of interstitial lung disease in systemic sclerosis: evidence-based European consensus statements.

BACKGROUND: Systemic sclerosis-associated interstitial lung disease (ILD) carries a high mortality risk; expert guidance is required to aid early recognition and treatment. We aimed to develop the first expert consensus and define an algorithm for the identification and management of the condition through application of well established methods. METHODS: Evidence-based consensus statements for systemic sclerosis-associated ILD management were established for six domains (ie, risk factors, screening, diagnosis and severity assessment, treatment initiation and options, disease progression, and treatment escalation) using a modified Delphi process based on a systematic literature analysis. A panel of 27 Europe-based pulmonologists, rheumatologists, and internists with expertise in systemic sclerosis-associated ILD participated in three rounds of online surveys, a face-to-face discussion, and a WebEx meeting, followed by two supplemental Delphi rounds, to establish consensus and define a management algorithm. Consensus was considered achieved if at least 80% of panellists indicated agreement or disagreement. FINDINGS: Between July 1, 2018, and Aug 27, 2019, consensus agreement was reached for 52 primary statements and six supplemental statements across six domains of management, and an algorithm was defined for clinical practice use. The agreed statements most important for clinical use included: all patients with systemic sclerosis should be screened for systemic sclerosis-associated ILD using high-resolution CT; high-resolution CT is the primary tool for diagnosing ILD in systemic sclerosis; pulmonary function tests support screening and diagnosis; systemic sclerosis-associated ILD severity should be measured with more than one indicator; it is appropriate to treat all severe cases; no pharmacological treatment is an option for some patients; follow-up assessments enable identification of disease progression; progression pace, alongside disease severity, drives decisions to escalate treatment. INTERPRETATION: Through a robust modified Delphi process developed by a diverse panel of experts, the first evidence-based consensus statements were established on guidance for the identification and medical management of systemic sclerosis-associated ILD. FUNDING: An unrestricted grant from Boehringer Ingelheim International.