Dataset Information

A real-time integrated framework to support clinical decision making for covid-19 patients.

ABSTRACT:

Background

The COVID-19 pandemic affected healthcare systems worldwide. Predictive models developed by Artificial Intelligence (AI) and based on timely, centralized and standardized real world patient data could improve management of COVID-19 to achieve better clinical outcomes. The objectives of this manuscript are to describe the structure and technologies used to construct a COVID-19 Data Mart architecture and to present how a large hospital has tackled the challenge of supporting daily management of COVID-19 pandemic emergency, by creating a strong retrospective knowledge base, a real time environment and integrated information dashboard for daily practice and early identification of critical condition at patient level. This framework is also used as an informative, continuously enriched data lake, which is a base for several on-going predictive studies.

Methods

The information technology framework for clinical practice and research was described. It was developed using SAS Institute software analytics tool and SAS® Vyia® environment and Open-Source environment R ® and Python ® for fast prototyping and modeling. The included variables and the source extraction procedures were presented.

Results

The Data Mart covers a retrospective cohort of 5528 patients with SARS-CoV-2 infection. People who died were older, had more comorbidities, reported more frequently dyspnea at onset, had higher d-dimer, C-reactive protein and urea nitrogen. The dashboard was developed to support the management of COVID-19 patients at three levels: hospital, single ward and individual care level.

Interpretation

The COVID-19 Data Mart based on integration of a large collection of clinical data and an AI-based integrated framework has been developed, based on a set of automated procedures for data mining and retrieval, transformation and integration, and has been embedded in the clinical practice to help managing daily care. Benefits from the availability of a Data Mart include the opportunity to build predictive models with a machine learning approach to identify undescribed clinical phenotypes and to foster hospital networks. A real-time updated dashboard built from the Data Mart may represent a valid tool for a better knowledge of epidemiological and clinical features of COVID-19, especially when multiple waves are observed, as well as for epidemic and pandemic events of the same nature (e. g. with critical clinical conditions leading to severe pulmonary inflammation). Therefore, we believe the approach presented in this paper may find several applications in comparable situations even at region or state levels. Finally, models predicting the course of future waves or new pandemics could largely benefit from network of DataMarts.

SUBMITTER: Murri R

PROVIDER: S-EPMC8800500 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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A real-time integrated framework to support clinical decision making for covid-19 patients.

Murri Rita R Masciocchi Carlotta C Lenkowicz Jacopo J Fantoni Massimo M Damiani Andrea A Marchetti Antonio A Sergi Paolo Domenico Angelo PDA Arcuri Giovanni G Cesario Alfredo A Patarnello Stefano S Antonelli Massimo M Bellantone Rocco R Bernabei Roberto R Boccia Stefania S Calabresi Paolo P Cambieri Andrea A Cauda Roberto R Colosimo Cesare C Crea Filippo F De Maria Ruggero R De Stefano Valerio V Franceschi Francesco F Gasbarrini Antonio A Landolfi Raffaele R Parolini Ornella O Richeldi Luca L Sanguinetti Maurizio M Urbani Andrea A Zega Maurizio M Scambia Giovanni G Valentini Vincenzo V

Computer methods and programs in biomedicine 20220129

<h4>Background</h4>The COVID-19 pandemic affected healthcare systems worldwide. Predictive models developed by Artificial Intelligence (AI) and based on timely, centralized and standardized real world patient data could improve management of COVID-19 to achieve better clinical outcomes. The objectives of this manuscript are to describe the structure and technologies used to construct a COVID-19 Data Mart architecture and to present how a large hospital has tackled the challenge of supporting dai ...[more]

PMID: 35158181

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Project description:Background and objectivesLung Ultrasound Score (LUS) identifies and monitors pneumonia by assigning increasing scores. However, it does not include parameters, such as inferior vena cava (IVC) diameter and index of collapse, diaphragmatic excursions and search for pleural and pericardial effusions. Therefore, we propose a new improved scoring system, termed "integrated" lung ultrasound score (i-LUS) which incorporates previously mentioned parameters that can help in prediction of disease severity and survival, choice of oxygenation mode/ventilation and assignment to subsequent areas of care in patients with COVID-19 pneumonia.MethodsUpon admission at the sub-intensive section of the emergency medical department (SEMD), 143 consecutively examined COVID-19 patients underwent i-LUS together with all other routine analysis. A database for anamnestic information, laboratory data, gas analysis and i-LUS parameters was created and analyzed.ResultsOf 143 enrolled patients, 59.4% were male (mean age 71 years) and 40.6% female. (mean age 79 years: p = 0.005). Patients that survived at 1 month had i-LUS score of 16, which was lower than that of non-survivors (median 20; p = 0.005). Survivors had a higher PaO2/FiO2 (median 321.5) compared to non-survivors (median 229, p < 0.001). There was a correlation between i-LUS and PaO2/FiO2 ratio (rho:-0.4452; p < 0.001), PaO2/FiO2 and survival status (rho:-0.3452; p < 0.001), as well as i-LUS score and disease outcome (rho:0.24; p = 0.005). In non-survivors, the serum values of different significant COVID indicators were severely expressed. The i-LUS score was higher (median 20) in patients who required non-invasive ventilation (NIV) than in those treated only by oxygen therapy (median 15.42; p = 0.003). The odds ratio for death outcome was 1.08 (confidence interval 1.02-1.15) for each point increased. At 1-month follow-up, 65 patients (45.5%) died and 78 (54.5%) survived. Patients admitted to the high critical ward had higher i-LUS score than those admitted to the low critical one (p < 0.003).Conclusionsi-LUS could be used as a helpful clinical tool for early decision-making in patients with COVID-19 pneumonia.

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Dataset Information

A real-time integrated framework to support clinical decision making for covid-19 patients.

Background

Methods

Results

Interpretation

Publications

A real-time integrated framework to support clinical decision making for covid-19 patients.

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