Internet of Things and New Technologies for Tracking Perioperative Patients With an Innovative Model for Operating Room Scheduling: Protocol for a Development and Feasibility Study.

BACKGROUND: Management of operating rooms is a critical point in health care organizations because surgical departments represent a significant cost in hospital budgets. Therefore, it is increasingly important that there is effective planning of elective, emergency, and day surgery and optimization of both the human and physical resources available, always maintaining a high level of care and health treatment. This would lead to a reduction in patient waiting lists and better performance not only of surgical departments but also of the entire hospital. OBJECTIVE: This study aims to automatically collect data from a real surgical scenario to develop an integrated technological-organizational model that optimizes operating block resources. METHODS: Each patient is tracked and located in real time by wearing a bracelet sensor with a unique identifier. Exploiting the indoor location, the software architecture is able to collect the time spent for every step inside the surgical block. This method does not in any way affect the level of assistance that the patient receives and always protects their privacy; in fact, after expressing informed consent, each patient will be associated with an anonymous identification number. RESULTS: The preliminary results are promising, making the study feasible and functional. Times automatically recorded are much more precise than those collected by humans and reported in the organization's information system. In addition, machine learning can exploit the historical data collection to predict the surgery time required for each patient according to the patient's specific profile. Simulation can also be applied to reproduce the system's functioning, evaluate current performance, and identify strategies to improve the efficiency of the operating block. CONCLUSIONS: This functional approach improves short- and long-term surgical planning, facilitating interaction between the various professionals involved in the operating block, optimizing the management of available resources, and guaranteeing a high level of patient care in an increasingly efficient health care system. TRIAL REGISTRATION: ClinicalTrials.gov NCT05106621; https://clinicaltrials.gov/ct2/show/NCT05106621. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/45477.

Machine learning in perioperative medicine: a systematic review.

BACKGROUND: Risk stratification plays a central role in anesthetic evaluation. The use of Big Data and machine learning (ML) offers considerable advantages for collection and evaluation of large amounts of complex health-care data. We conducted a systematic review to understand the role of ML in the development of predictive post-surgical outcome models and risk stratification. METHODS: Following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines, we selected the period of the research for studies from 1 January 2015 up to 30 March 2021. A systematic search in Scopus, CINAHL, the Cochrane Library, PubMed, and MeSH databases was performed; the strings of research included different combinations of keywords: "risk prediction," "surgery," "machine learning," "intensive care unit (ICU)," and "anesthesia" "perioperative." We identified 36 eligible studies. This study evaluates the quality of reporting of prediction models using the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) checklist. RESULTS: The most considered outcomes were mortality risk, systemic complications (pulmonary, cardiovascular, acute kidney injury (AKI), etc.), ICU admission, anesthesiologic risk and prolonged length of hospital stay. Not all the study completely followed the TRIPOD checklist, but the quality was overall acceptable with 75% of studies (Rev #2, comm #minor issue) showing an adherence rate to TRIPOD more than 60%. The most frequently used algorithms were gradient boosting (n = 13), random forest (n = 10), logistic regression (LR; n = 7), artificial neural networks (ANNs; n = 6), and support vector machines (SVM; n = 6). Models with best performance were random forest and gradient boosting, with AUC > 0.90. CONCLUSIONS: The application of ML in medicine appears to have a great potential. From our analysis, depending on the input features considered and on the specific prediction task, ML algorithms seem effective in outcomes prediction more accurately than validated prognostic scores and traditional statistics. Thus, our review encourages the healthcare domain and artificial intelligence (AI) developers to adopt an interdisciplinary and systemic approach to evaluate the overall impact of AI on perioperative risk assessment and on further health care settings as well.

COVID-19 respiratory support outside the ICU's doors. An observational study for a new operative strategy.

BACKGROUND AND AIM: During the first wave of the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) pandemic, we faced a massive clinical and organizational challenge having to manage critically ill patients outside the Intensive Care Unit (ICU). This was due to the significant imbalance between ICU bed availability and the number of patients presenting Acute Hypoxemic Respiratory Failure caused by SARS-CoV-2-related interstitial pneumonia. We therefore needed to perform Non-Invasive Ventilation (NIV) in non-intensive wards to assist these patients and relieve pressure on the ICUs and subsequently implemented a new organizational and clinical model. This study was aimed at evaluating its effectiveness and feasibility. METHODS: We recorded the anamnestic, clinical and biochemical data of patients undergoing non-invasive mechanical ventilation while hospitalized in non-intensive CoronaVirus Disease 19 (COVID-19) wards. Data were registered on admission, during anesthesiologist counseling, and when NIV was started and suspended. We retrospectively registered the available results from routine arterial blood gas and laboratory analyses for each time point. RESULTS: We retrospectively enrolled 231 patients. Based on our criteria, we identified 46 patients as NIV responders, representing 19.9% ​​of the general study population and 29.3% of the patients that spent their entire hospital stay in non-ICU wards. Overall mortality was 56.2%, with no significant differences between patients in non-intensive wards (57.3%) and those later admitted to the ICU (54%) Conclusions: NIV is safe and manageable in an emergency situation and could become part of an integrated clinical and organizational model.

COVID-19 What are drugs and strategies now?

From February 2019 the World faces the Covid19 pandemic. The data in our possession are still insufficient to effectively combat this pathology. The gold standard for diagnosis remains molecular testing, while clinical and instrumental and serological diagnostics are highly nonspecific leading to a slowdown in the battle against covid19.[3] Can Artificial Intelligence (AI) and Machine Learning (ML) help us? The use of large databases to cross-reference data to stratify the diagnostic scores, to quickly differentiate a critical Covid-19 patient from a non-critical one is the challenge of the future. All to achieve better management of resources in the field and a more effective therapeutic approach.[2].