OBJECTIVE To explore comprehensive management and potential issues associated with long-term prescriptions medications of psychiatry， in order to provide a reference for the comprehensive management of long-term prescriptions of psychiatry in psychiatric hospitals and other medical institutions’ pharmacies. METHODS Starting from the applicable principles for long-term prescriptions of psychiatry， this study introduced the standardized assessment and precautions before issuing long-term prescriptions， the formulation and adjustment of the drug list， as well as the rational management of the long-term prescriptions. It also analyzed potential issues that may arise in the comprehensive management of long-term prescription medications and proposed corresponding countermeasures and suggestions. RESULTS & CONCLUSIONS Prior to initiating long-term prescriptions， a standardized assessment should be conducted on patients from the aspects of their psychiatric condition and long-term potential risk factors， pharmacological treatment plans and other non-pharmacological therapies， physical illnesses. Additionally， healthcare providers should fulfill their obligation to inform patients or their family members. The comprehensive management of long-term prescription medications should be jointly established and improved by multiple departments， and the formulation of drug catalogs should avoid including drugs with potential social harm or medication risks while complying with policy requirements. Furthermore， measures such as adding special identifiers to long-term prescriptions， providing patients with reminders about （No.YGLX202537） prescription expiration， or offering online consultations can also effectively enhance the rationality of medication use under long-term prescriptions. Currently， the implementation of long-term prescriptions in psychiatry remains challenged by inconsistencies in prescription duration， incomplete coverage of diagnostic categories， poor patient adherence， and the risk of deviation in clinical assessments. In this regard， measures such as collaborating with multiple departments to strengthen long-term prescription information management， providing matching pharmaceutical services， ensuring the quality and rationality of long-term prescription implementation， and using modern methods to screen high-risk patients can be taken to improve patient medication compliance and safety.

OBJECTIVE: To develop and compare risk prediction models for in-hospital post-cardiac arrest brain injury (PCABI) in critically ill patients using nomograms and random forest algorithms, aiming to identify the optimal model for early identification of high-risk PCABI patients and providing evidence for precise treatment. METHODS: A retrospective cohort study was used to collect the first-time in-hospital cardiac arrest (IHCA) patients admitted to the intensive care unit (ICU) from 2008 to 2019 in the Medical Information Mart for Intensive Care-IV (MIMIC-IV) as the study population, and the patients' age, gender, body mass, health insurance utilization, first vital signs and laboratory tests within 24 hours of ICU admission, mechanical ventilation, and critical care scores were extracted. Independent influencing factors of PCABI were identified through univariate and multivariate Logistic regression analyses. The included patients were randomly divided into a training cohort and an internal validation cohort in a 7:3 ratio, and the PCABI risk prediction model was constructed by the nomogram and random forest algorithm, respectively, and the model was evaluated by receiver operator characteristic curve (ROC curve), the calibration curve, and the decision curve analysis (DCA), and after the better model was selected, 179 patients admitted to Tianjin Medical University General Hospital as the external validation cohort for external evaluation were collected by using the same inclusion and exclusion criteria. RESULTS: A total of 1 419 patients with without traumatic brain injury who had their first-time IHCA were enrolled, including 995 in the training cohort (including 176 PCABI and 819 non-PCABI) and 424 in the internal validation cohort (including 74 PCABI and 350 non-PCABI). Univariate and multivariate analysis showed that age, potassium, urea nitrogen, sequential organ failure assessment (SOFA), acute physiology and chronic health evaluation III (APACHE III), and mechanical ventilation were independent influences on the occurrence of PCABI in patients with IHCA (all P < 0.05). Combining the above variables, we constructed a nomogram model and a random forest model for comparison, and the results show that the nomogram model has better predictive efficacy than the random forest model [nomogram model: area under the ROC curve (AUC) of the training cohort = 0.776, with a 95% credible interval (95%CI) of 0.741-0.811; internal validation cohort AUC = 0.776, with a 95%CI of 0.718-0.833; random forest model: AUC = 0.720, with a 95%CI of 0.653-0.787], and they performed similarly in terms of calibration curves, but the nomogram performed better in terms of decision curve analysis (DCA); at the same time, the nomogram model was robust in terms of external validation cohort (external validation cohort AUC = 0.784, 95%CI was 0.692-0.876). CONCLUSIONS A nomogram risk prediction model for the occurrence of PCABI in critically ill patients was successfully constructed, which performs better than the random forest model, helps clinicians to identify the risk of PCABI in critically ill patients at an early stage and provides a theoretical basis for early intervention.
Humans ; Critical Illness ; Retrospective Studies ; Heart Arrest/complications* ; Nomograms ; Brain Injuries/etiology* ; Intensive Care Units ; Algorithms ; Male ; Female ; Middle Aged ; ROC Curve ; Risk Factors ; Risk Assessment ; Logistic Models ; Aged