Sepsis is a major cause of mortality among hospitalized patients worldwide. Shorter time to administration of broad-spectrum antibiotics is associated with improved outcomes, but early recognition of sepsis remains a major challenge. In a two-center cohort study with prospective sample collection from 1400 adult patients in emergency departments suspected of sepsis, we sought to determine the diagnostic and prognostic capabilities of a machine-learning algorithm based on clinical data and a set of uncommonly measured biomarkers. Specifically, we demonstrate that a machine-learning model developed using this dataset outputs a score with not only diagnostic capability but also prognostic power with respect to hospital length of stay (LOS), thirty-day mortality, and thirty-day inpatient readmission both in our entire testing cohort and various subpopulations. The area under the Receiver Operating Curve (AUROC) for diagnosis of sepsis was 0.83. Predicted risk scores for patients with septic shock were higher compared to patients with sepsis but without shock (p < 0.0001). Scores for patients with infection and organ dysfunction were higher compared to those without either condition (p < 0.0001). Stratification based on predicted scores of the patients into low, medium and high-risk groups showed significant differences in length of stay (p < 0.0001), thirty-day mortality (p < 0.0001), and thirty-day inpatient readmission (p < 0.0001). In conclusion, a machine-learning algorithm based on EMR data and three non-routinely measured biomarkers demonstrated good diagnostic and prognostic capability at the time of initial blood culture.
SARS-CoV2, also known as COVID-19 has altered the course of many aspects of patient care. The aim of this study is to determine mental health implications of COVID-19 for hospitalized patients, and their care received. METHODS: A single institution survey was given to COVID-19 inpatients at Carle Foundation Hospital. The survey was a standardized questionnaire collecting information regarding patient mood, contributing factors to mood and barriers to care during their hospitalization. 47 patients were contacted, and a total of 28 responses were collected. The timing of the study was during the peak of COVID-19 infection in the United States. The survey included open-ended, multiple-choice questions pertaining to mood during their recent hospitalization, and comparisons with previous hospitalizations if applicable. Participants were allowed to choose more than one option for each question regarding mood and barriers to care, and a final free response question was included for further clarification. RESULTS: Of the 28 patients, 75% expressed distinct mood disturbance during their hospital admission. Of patients experiencing mood disturbance, 38% were men and 67% were women. 71% of patients with mood disturbance had been hospitalized previously and 59% of previously hospitalized patients experienced increased mental stress during their COVID admission compared to prior medical admission(s). The most commonly identified factors which increased anxiety were uncertainty disease progression (57% of participants), absence of family (50%), and isolation rooms (40%). CONCLUSIONS: The current study demonstrates that 75% of COVID inpatients at our institution experienced increased stress. Multiple factors play a role in patient attitude including the uncertainty of disease progression, strict restriction of visitation from family members, and inability to develop open connection with health care workers due to use of personal protective equipment. CLINICAL IMPLICATIONS: The pandemic's rapid spread, high mortality rate and limited information about the virus was expected to lead to significant mental health ramifications. Our study provided data suggestive of increased mental health distress in inpatients during the COVID pandemic. Suggestions to improve patient satisfaction include the use of social support systems, clear communication with the health care team and virtual interaction with family. It is imperative that we communicate clearly with our patients in order to mitigate anxiety. As demonstrated previously, a better grasp on information and greater knowledge of the circumstances leads to lower anxiety during pandemic situations (Mishra). Use of anxiolytics, such as hydroxyzine has shown possible aid in inpatient hospital admissions, however it is a field that needs to be explored further. It is our role as health care workers to recognize the negative effects and implement improvement going forward.
BackgroundCoronavirus disease 2019 (COVID-19) infection is associated with troponin elevation, which is associated with increased mortality. However, it is not clear if troponin elevation is independently linked to increased mortality in COVID-19 patients. Although there is considerable literature on risk factors for mortality in COVID-19-associated myocardial injury, the Global Registry of Acute Coronary Events (GRACE), Thrombolysis in Myocardial Infarction (TIMI), and Sequential Organ Failure Assessment (SOFA) scores have not been studied in COVID-19-related myocardial injury. This data is important in risk-stratifying COVID-19 myocardial injury patients.
INTRODUCTION: Metastasis is a complex aspect of malignancy that is constantly being studied and monitored in advanced cases. In most cases, different types of cancers can be predictable in how they metastasize, and this can affect management and treatment.
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