Objective: To evaluate Mortality Probability Model (MPM) IIo as a tool to predict very poor prognosis after intensive care unit admission.
Methods: The study was conducted as a prospective observational study in a medical-surgical intensive care unit in a tertiary care teaching hospital, Riyadh, Kingdom of Saudi Arabia. Data necessary to calculate MPM IIo predicted mortality was collected from March 1999 through to February 2000 on all intensive care unit admissions. The hospital outcome was documented. We calculated the sensitivity, specificity, positive predictive value and negative predictive value of MPM IIo using cutoff points of 90% and 95%.
Results: Data was complete on 557/569 patients (98%). Thirty-one patients had predicted mortality of >95% and all died yielding a specificity of 100% and positive predictive value of 100%. However, sensitivity was only 18% and negative predictive value 73%. Forty-four patients had predicted mortality of >90% of whom only one survived yielding a specificity of 99.7% and a positive predictive value of 97.7%. Sensitivity was only 25% and negative predictive value of 75%.
Conclusions: Using a decision-cutoff of 95% predicted mortality using MPMI IIo had a very high specificity in predicting death after intensive care unit admission, although with a low sensitivity. This information can be used to support clinical judgment regarding the very ill patients who are unlikely to benefit from intensive care unit admission.
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JMIR Hum Factors
January 2025
Women's Health Research Institute, Vancouver, BC, Canada.
Background: Digital health innovations provide an opportunity to improve access to care, information, and quality of care during the perinatal period, a critical period of health for mothers and infants. However, research to develop perinatal digital health solutions needs to be informed by actual patient and health system needs in order to optimize implementation, adoption, and sustainability.
Objective: Our aim was to co-design a research agenda with defined research priorities that reflected health system realities and patient needs.
JMIR Res Protoc
January 2025
Institute for Health Care Management and Research, University of Duisburg-Essen, Essen, Germany.
Background: Artificial intelligence (AI)-based clinical decision support systems (CDSS) have been developed for several diseases. However, despite the potential to improve the quality of care and thereby positively impact patient-relevant outcomes, the majority of AI-based CDSS have not been adopted in standard care. Possible reasons for this include barriers in the implementation and a nonuser-oriented development approach, resulting in reduced user acceptance.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Department of Radiation Oncology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Background: Primary intracranial germ cell tumors (iGCTs) are highly malignant brain tumors that predominantly occur in children and adolescents, with an incidence rate ranking third among primary brain tumors in East Asia (8%-15%). Due to their insidious onset and impact on critical functional areas of the brain, these tumors often result in irreversible abnormalities in growth and development, as well as cognitive and motor impairments in affected children. Therefore, early diagnosis through advanced screening techniques is vital for improving patient outcomes and quality of life.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Department of Gastroenterology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China.
Background: Gastrointestinal bleeding (GIB) is a severe and potentially life-threatening complication in patients with acute myocardial infarction (AMI), significantly affecting prognosis during hospitalization. Early identification of high-risk patients is essential to reduce complications, improve outcomes, and guide clinical decision-making.
Objective: This study aimed to develop and validate a machine learning (ML)-based model for predicting in-hospital GIB in patients with AMI, identify key risk factors, and evaluate the clinical applicability of the model for risk stratification and decision support.
JMIR Res Protoc
January 2025
Department of Medicine and Optometry, eHealth Institue, Linnaeus University, Kalmar, Sweden.
Background: Health worker migration from Nigeria poses significant challenges to the Nigerian health care sector and has far-reaching implications for health care systems globally. Understanding the factors driving migration, its effects on health care delivery, and potential policy interventions is critical for addressing this complex issue.
Objective: This study aims to comprehensively examine the factors encouraging the emigration of Nigerian health workers, map out the effects of health worker migration on the Nigerian health system, document the loss of investment in health training and education resulting from migration, identify relevant policy initiatives addressing migration, determine the effects of Nigerian health worker migration on destination countries, and identify the benefits and demerits to Nigeria of health worker migration.
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