Plos One
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Outcome measurement is fundamental to assess needs and priority of care in palliative care settings. The Integrated Palliative care Outcome Scale (IPOS) was developed from earlier versions of this tool. Its use is encouraged to ameliorate the assessment of individual outcomes in palliative care settings. This study aimed to translate and culturally adapt IPOS into Italian, and explore its face and content validity. ⋯ The Italian IPOS, in its four versions directed to patients or staff and with a recall period of 3 or 7 days, has face and content validity for use in clinical settings and is ready for further psychometric and clinimetric validation.
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Degenerative cervical myelopathy (DCM) is a spinal cord condition that results in progressive non-traumatic compression of the cervical spinal cord. Spine surgeons must consider a large quantity of information relating to disease presentation, imaging features, and patient characteristics to determine if a patient will benefit from surgery for DCM. We applied a supervised machine learning approach to develop a classification model to predict individual patient outcome after surgery for DCM. ⋯ Worse pre-operative disease severity, longer duration of DCM symptoms, older age, higher body weight, and current smoking status were associated with worse surgical outcomes. We developed a model that predicted positive surgical outcome for DCM with good accuracy at the individual patient level on an independent testing cohort. Our analysis demonstrates the applicability of machine-learning to predictive modeling in spine surgery.
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Observational Study
Combining patient visual timelines with deep learning to predict mortality.
Deep learning algorithms have achieved human-equivalent performance in image recognition. However, the majority of clinical data within electronic health records is inherently in a non-image format. Therefore, creating visual representations of clinical data could facilitate using cutting-edge deep learning models for predicting outcomes such as in-hospital mortality, while enabling clinician interpretability. The objective of this study was to develop a framework that first transforms longitudinal patient data into visual timelines and then utilizes deep learning to predict in-hospital mortality. ⋯ We converted longitudinal patient data into visual timelines and applied a deep neural network for predicting in-hospital mortality more accurately than current standard clinical models, while allowing for interpretation. Our framework holds promise for predicting several important outcomes in clinical medicine.
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Observational Study
Prevalence of frailty in a tertiary hospital: A point prevalence observational study.
Frailty is an important concept in modern healthcare due to its association with adverse outcomes. Its prevalence varies in the literature and there is a paucity of literature looking at the prevalence of frailty in an inpatient setting. Its significance lies on its impact on resource utilisation and costs. ⋯ Frailty is highly prevalent in the hospital setting with 48.8% of all inpatients classified as frail. This high number of frail patients has significant resource implications and an increased understanding of the burden of frailty in this population may aid targeting of interventions towards this vulnerable population.
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The information technology is a pivotal source of communication between patients and healthcare providers for managing chronic diseases. The objective of this study is to assess the capacity and willingness of patients to use information technology for managing chronic diseases. ⋯ This study concluded that nearly half of the respondents were willing to use text messages; whereas, majority was reluctant in using video conference and e-mail as a source of communication with healthcare providers. Most of the respondents who were located farther from the health care provider were willing to use video conferencing in case it could save more than 60 minutes of their time.