![]() ![]() Both regression and machine learning methods provide predictions for individual patients. Regression modeling is the most common approach, while machine learning techniques are gaining interest. A second example is on computed tomography (CT) decision rules in patients with minor head injury ( 4).ĭevelopment of a prognostic model needs to consider various steps, such as the specification and coding of predictors for the model, and how to estimate the model parameters ( 5). These models combine clinical, radiological and laboratory admission characteristics to predict risk of mortality and unfavorable outcome ( 3). For illustration, we consider the validation of the International Mission for Prognosis and Analysis of Clinical Trials in Traumatic Brain Injury (IMPACT) prognostic models for patients with moderate and severe traumatic brain injury. In this viewpoint, we focus on design and analysis of validation studies for prognostic models. ![]() Although guidelines have been proposed to improve development and reporting of prognostic models, a majority of the published models is not thoroughly validated ( 1, 2). Before application in clinical practice, prognostic models should be validated to judge their generalizability. Prognostic models can be applied in research and clinical practice, for instance to assist clinicians with decisions regarding treatment choices or informing patients and family members on prognosis ( 1). Multivariable prognostic models combine several characteristics to provide predictions for individual patients. Email: 01 October 2018 Accepted: 18 October 2018 Published: 05 November 2018. Department of Public Health, Erasmus MC-University Medical Center, PO box 2040, 3000 CA Rotterdam, the Netherlands. Policy of Dealing with Allegations of Research MisconductĬorrespondence to: Simone Dijkland, MD.Policy of Screening for Plagiarism Process.Conclusion The continuous decrease of FT3 level and the continuous increase of FT4 and TSH levels are potentially associated with the poor prognosis of patients with hemorrhagic stroke, which is worthy of clinical attention. With the decrease of the patient's age, the serum FT3 level has a gradually increasing trend, and the serum FT4, TSH level, mortality and rebleeding rate have a gradually decreasing trend (P<0.05). The TSH level of the case group was significantly higher than that of the control group at all times (P0.05). The FT4 level of the case group was significantly higher than that of the control group at all times (P0.05). Results There were no statistically significant differences in gender and age between the three groups (P>0.05), but the differences in hypertension, hyperglycemia, and hyperlipidemia were statistically significant (P0.05). The epidemiological characteristics of patients with different prognosis and the dynamic change trend of FT3, FT4 and TSH in the same serum were compared. At the end of the follow-up, the subjects were divided into three groups death, rebleeding, and no adverse prognosis according to the prognostic outcome of the 12-month short-term follow-up. The assay method is enzyme-linked immunosorbent assay. Observe the trend characteristics of dynamic changes. Collect and analyze prognosis ( death, rebleeding, no adverse prognosis) at baseline and 12 months of follow-up monitor serum FT3, FT4, and TSH levels during treatment at the same time during follow-up, 7 days after treatment, and 14 days after treatment. Methods From January 2017 to January 2020, 227 patients with multicenter hemorrhagic stroke in our hospital were selected for analysis. Objective To discuss the epidemiological characteristics of the dynamic changes of serum FT3, FT4, and TSH levels in patients with hemorrhagic stroke under the age of 45, and to discuss the prognostic evaluation effects and influencing factors of these indicators. ![]()
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