Plos One
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Existing prediction models for acute respiratory distress syndrome (ARDS) require manual chart abstraction and have only fair performance-limiting their suitability for driving clinical interventions. We sought to develop a machine learning approach for the prediction of ARDS that (a) leverages electronic health record (EHR) data, (b) is fully automated, and (c) can be applied at clinically relevant time points throughout a patient's stay. ⋯ We developed an ARDS prediction model based on EHR data with good discriminative performance. Our results demonstrate the feasibility of a machine learning approach to risk stratifying patients for ARDS solely from data extracted automatically from the EHR.
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Clinical Trial
Resilience in middle-aged partners of patients diagnosed with incurable cancer: A thematic analysis.
Providing care for patients with advanced cancer is often the responsibility of the partner. Being confronted with an incurable cancer diagnosis can be highly disruptive for the patient's partner and can be considered a potentially traumatic event. However, most caregivers seem to adapt well during the process of providing care. This finding is in line with the concept of resilience in literature: a dynamic process of adapting well, resulting from the interplay between intrinsic and extrinsic resources and risks. Resilience is age-related, with the elderly population being higher in resilience as compared to the younger generation. However, resilience has been understudied in middle-aged caregivers. ⋯ A resilient trajectory results from an interplay between individual and contextual resources. To build resilience in middle-aged partners of patients with incurable cancer, health care professionals should address all available resources. Moreover, they should be aware of being part of the caregiver's context, a complex adaptive system that can be either resilience-supporting or -threatening.
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Although a wide range of needs assessment tools for cancer patients have been developed, no standardized and commonly accepted instruments were recommended to use in clinical care. This systematic review was conducted to assess the quality of psychometric properties of needs assessment tools among cancer patients in order to help oncology healthcare professionals select the most appropriate needs assessment tools in routine clinical practice. ⋯ Despite several needs assessment tools exist to assess care needs in cancer patients, further improvement of already existing and promising instruments is recommended.
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Acute kidney injury (AKI) is a common complication after surgery that is associated with increased morbidity and mortality. The majority of existing perioperative AKI risk prediction models are limited in their generalizability and do not fully utilize intraoperative physiological time-series data. Thus, there is a need for intelligent, accurate, and robust systems to leverage new information as it becomes available to predict the risk of developing postoperative AKI. ⋯ Postoperative AKI prediction was improved with high sensitivity and specificity through a machine learning approach that dynamically incorporated intraoperative data.
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Posthepatectomy liver failure (PHLF) is the most leading cause of mortality following hepatectomy in patients with hepatocellular carcinoma (HCC). Platelet count was reported to be a simple but useful indicator of liver cirrhosis and function of spleen. Spleen stiffness (SS) was used to evaluate the morphological change of spleen and was reported to be related to liver cirrhosis and portal hypertension. ⋯ PHLF occured in 23 (14.6%) patients. PSR (P<0.001, odds ratio (OR) = 0.622, 95% confidence interval (CI) 0.493~0.784), hepatic inflow occlusion (HIO) (P = 0.003, OR = 1.044, 95% CI 1.015~1.075) and major hepatectomy (P = 0.019, OR = 5.967, 95% CI 1.346~26.443) were demonstrated to be the independent predictive factors for development of PHLF in a multivariate analysis. Results of the present study suggested PSR is a novel and non-invasive model for predicting PHLF in patients with HCC.