Predicting intensive care unit-acquired weakness:A multilayer perceptron neural network approach

In this editorial,we comment on the article by Wang and Long,published in a recent issue of the World Journal of Clinical Cases.The article addresses the challenge of predicting intensive care unit-acquired weakness(ICUAW),a neuromuscular disorder affecting critically ill patients,by employing a novel processing strategy based on repeated machine learning.The editorial presents a dataset comprising clinical,demographic,and laboratory variables from intensive care unit(ICU)patients and employs a multilayer perceptron neural network model to predict ICUAW.The authors also performed a feature importance analysis to identify the most relevant risk factors for ICUAW.This editorial contributes to the growing body of literature on predictive modeling in critical care,offering insights into the potential of machine learning approaches to improve patient outcomes and guide clinical decision-making in the ICU setting.

Machine learning insights on intensive care unit-acquired weakness

Intensive care unit-acquired weakness(ICU-AW)significantly hampers patient recovery and increases morbidity.With the absence of established preventive strategies,this study utilizes advanced machine learning methodologies to unearth key predictors of ICU-AW.Employing a sophisticated multilayer perceptron neural network,the research methodically assesses the predictive power for ICU-AW,pinpointing the length of ICU stay and duration of mechanical ventilation as pivotal risk factors.The findings advocate for minimizing these elements as a preventive approach,offering a novel perspective on combating ICU-AW.This research illuminates critical risk factors and lays the groundwork for future explorations into effective prevention and intervention strategies.

Significant risk factors for intensive care unit-acquired weakness:A processing strategy based on repeated machine learning

BACKGROUND Intensive care unit-acquired weakness(ICU-AW)is a common complication that significantly impacts the patient's recovery process,even leading to adverse outcomes.Currently,there is a lack of effective preventive measures.AIM To identify significant risk factors for ICU-AW through iterative machine learning techniques and offer recommendations for its prevention and treatment.METHODS Patients were categorized into ICU-AW and non-ICU-AW groups on the 14th day post-ICU admission.Relevant data from the initial 14 d of ICU stay,such as age,comorbidities,sedative dosage,vasopressor dosage,duration of mechanical ventilation,length of ICU stay,and rehabilitation therapy,were gathered.The relationships between these variables and ICU-AW were examined.Utilizing iterative machine learning techniques,a multilayer perceptron neural network model was developed,and its predictive performance for ICU-AW was assessed using the receiver operating characteristic curve.RESULTS Within the ICU-AW group,age,duration of mechanical ventilation,lorazepam dosage,adrenaline dosage,and length of ICU stay were significantly higher than in the non-ICU-AW group.Additionally,sepsis,multiple organ dysfunction syndrome,hypoalbuminemia,acute heart failure,respiratory failure,acute kidney injury,anemia,stress-related gastrointestinal bleeding,shock,hypertension,coronary artery disease,malignant tumors,and rehabilitation therapy ratios were significantly higher in the ICU-AW group,demonstrating statistical significance.The most influential factors contributing to ICU-AW were identified as the length of ICU stay(100.0%)and the duration of mechanical ventilation(54.9%).The neural network model predicted ICU-AW with an area under the curve of 0.941,sensitivity of 92.2%,and specificity of 82.7%.CONCLUSION The main factors influencing ICU-AW are the length of ICU stay and the duration of mechanical ventilation.A primary preventive strategy,when feasible,involves minimizing both ICU stay and mechanical ventilation duration.

Unveiling significant risk factors for intensive care unit-acquired weakness:Advancing preventive care

In this editorial,we discuss an article titled,“Significant risk factors for intensive care unit-acquired weakness:A processing strategy based on repeated machine learning,”published in a recent issue of the World Journal of Clinical Cases.Intensive care unit-acquired weakness(ICU-AW)is a debilitating condition that affects critically ill patients,with significant implications for patient outcomes and their quality of life.This study explored the use of artificial intelligence and machine learning techniques to predict ICU-AW occurrence and identify key risk factors.Data from a cohort of 1063 adult intensive care unit(ICU)patients were analyzed,with a particular emphasis on variables such as duration of ICU stay,duration of mechanical ventilation,doses of sedatives and vasopressors,and underlying comorbidities.A multilayer perceptron neural network model was developed,which exhibited a remarkable impressive prediction accuracy of 86.2%on the training set and 85.5%on the test set.The study highlights the importance of early prediction and intervention in mitigating ICU-AW risk and improving patient outcomes.

Risk factors for intensive-care-unit-acquired weakness

Wang et al reported 1063 cases from the initial 14 d of intensive care unit(ICU)stay,and analyzed relevant data such as age,comorbidities,recent dosages,vapor pressure dosages,duration of mechanical ventilation,length of ICU stay,and rehabilitation therapy,which are closely related to ICU-acquired weakness(ICUAW).It is suggested that the length of ICU stay and the duration of mechanical ventilation are the main factors.ICU-AW is the most common neuromuscular injury in the ICU,which affects clinical progression and outcomes of patients.This manuscript helps to improve the early recognition of ICU-AW,thereby reducing mortality and improving prognosis.

Evaluating neuromuscular electrical stimulation for preventing and managing intensive care unit-acquired weakness:Current evidence and future directions

Intensive care unit-acquired weakness(ICU-AW)is a prevalent issue in critical care,leading to significant muscle atrophy and functional impairment.Aiming to address this,Neuromuscular Electrical Stimulation(NMES)has been explored as a therapy.This systematic review assesses NMES's safety and effectiveness in enhancing functional capacity and mobility in pre-and post-cardiac surgery patients.NMES was generally safe and feasible,with intervention sessions varying in frequency and duration.Improvements in muscle strength and 6-minute walking test distances were observed,particularly in preoperative settings,but postoperative benefits were inconsistent.NMES showed promise in preventing muscle loss and improving strength,although its impact on overall functional capacity remained uncertain.Challenges such as short ICU stays and body composition affecting NMES efficacy were noted.NMES also holds potential for other conditions like cerebral palsy and stroke.Further research is needed to optimize NMES protocols and better understand its full benefits in preventing ICU-AW and improving patient outcomes.

Individualized intensive insulin therapy of diabetes: Not only thegoal, but also the time

Intensive insulin therapy has been extensively used to control blood glucose levels because of its ability to reduce the risk of chronic complications of diabetes.According to current guidelines,intensive glycemic control requires individu-alized glucose goals rather than as low as possible.During intensive therapy,rapid blood glucose reduction can aggravate microvascular and macrovascular complications,and prolonged overuse of insulin can lead to treatment-induced neuropathy and retinopathy,hypoglycemia,obesity,lipodystrophy,and insulin antibody syndrome.Therefore,we need to develop individualized hypoglycemic plans for patients with diabetes,including the time required for blood glucose normalization and the duration of intensive insulin therapy,which deserves further study.

Evaluation of the effectiveness of preventive nursing measures for pressure injuries in patients in the neurology intensive care unit

BACKGROUND Patients in neurology intensive care units(ICU)are prone to pressure injuries(PU)due to factors such as severe illness,long-term bed rest,and physiological dysfunction.PU not only causes pain and complications to patients,but also increases medical burden,prolongs hospitalization time,and affects the recovery process.AIM To evaluate and optimize the effectiveness of pressure injury prevention nursing measures in neurology ICU patients.METHODS A retrospective study was conducted,and 60 patients who were admitted to the ICU of the Department of Neurology were selected and divided into an observation group and a control group according to the order of admission,with 30 people in each group.The observation group implemented pressure injury prevention and nursing measures,while the control group adopted routine care.RESULTS Comparison between observation and control groups following pressure injury prevention nursing intervention revealed significantly lower incidence rates in the observation group compared to the control group at 48 h(8.3%vs 26.7%),7 d(16.7%vs 43.3%),and 14 d(20.0%vs 50.0%).This suggests a substantial reduction in pressure injury incidence in the observation group,with the gap widening over time.Additionally,patients in the observation group exhibited quicker recovery,with a shorter average time to get out of bed(48 h vs 72 h)and a shorter average length of stay(12 d vs 15 d)compared to the control group.Furthermore,post-intervention,patients in the observation group reported significantly improved quality of life scores,including higher scores in body satisfaction,feeling and function,and comfort(both psychological and physiological),indicating enhanced overall well-being and comfort following the implementation of pressure injury prevention nursing measures.CONCLUSION Implementing pressure injury preventive care measures for neurology ICU patients will have better results.

Discharging patients home from the intensive care unit:A new trend

Discharging patients directly to home from the intensive care unit(ICU)is becoming a new trend.This review examines the feasibility,benefits,challenges,and considerations of directly discharging ICU patients.By analyzing available evidence and healthcare professionals'experiences,the review explores the potential impacts on patient outcomes and healthcare systems.The practice of direct discharge from the ICU presents both opportunities and complexities.While it can potentially reduce costs,enhance patient comfort,and mitigate complications linked to extended hospitalization,it necessitates meticulous patient selection and robust post-discharge support mechanisms.Implementing this strategy successfully mandates the availability of home-based care services and a careful assessment of the patient's readiness for the transition.Through critical evaluation of existing literature,this review underscores the significance of tailored patient selection criteria and comprehensive post-discharge support systems to ensure patient safety and optimal recovery.The insights provided contribute evidence-based recommendations for refining the direct discharge approach,fostering improved patient outcomes,heightened satisfaction,and streamlined healthcare processes.Ultimately,the review seeks to balance patientcentered care and effective resource utilization within ICU discharge strategies.

Study of the intensive care unit activity scale in the early rehabilitation of patients after direct cardiac surgery

BACKGROUND Direct cardiac surgery often necessitates intensive post-operative care,and the intensive care unit(ICU)activity scale represents a crucial metric in assessing and guiding early rehabilitation efforts to enhance patient recovery.AIM To clarify the clinical application value of the ICU activity scale in the early recovery of patients after cardiac surgery.METHODS One hundred and twenty patients who underwent cardiac surgery between September 2020 and October 2021 were selected and divided into two groups using the random number table method.The observation group was rated using the ICU activity scale and the corresponding graded rehabilitation interventions were conducted based on the ICU activity scale.The control group was assessed in accordance with the routine rehabilitation activities,and the postoperative rehabilitation indexes of the patients in both groups were compared(time of tracheal intubation,time of ICU admission,occurrence of complications,and activity scores before ICU transfer).The two groups were compared according to postoperative rehabilitation indicators(time of tracheal intubation,length of ICU stay,and occurrence of complications)and activity scores before ICU transfer.RESULTS In the observation group,tracheal intubation time lasted for 18.30±3.28 h and ICU admission time was 4.04±0.83 d,which were significantly shorter than the control group(t-values:2.97 and 2.038,respectively,P<0.05).The observation group also had a significantly lower number of complications and adverse events compared to the control group(P<0.05).Before ICU transfer,the observation group(6.7%)had few complications and adverse events than the control group(30.0%),and this difference was statistically significant(P<0.05).Additionally,the activity score was significantly higher in the observation(26.89±0.97)compared to the control groups(22.63±1.12 points)(t-value;-17.83,P<0.05).CONCLUSION Implementation of early goal-directed activities in patients who underwent cardiac surgery using the ICU activity scale can promote the recovery of cardiac function.

Pioneering role of machine learning in unveiling intensive care unitacquired weakness

In the research published in the World Journal of Clinical Cases,Wang and Long conducted a quantitative analysis to delineate the risk factors for intensive care unit-acquired weakness(ICU-AW)utilizing advanced machine learning methodologies.The study employed a multilayer perceptron neural network to accurately predict the incidence of ICU-AW,focusing on critical variables such as ICU stay duration and mechanical ventilation.This research marks a significant advancement in applying machine learning to clinical diagnostics,offering a new paradigm for predictive medicine in critical care.It underscores the importance of integrating artificial intelligence technologies in clinical practice to enhance patient management strategies and calls for interdisciplinary collaboration to drive innovation in healthcare.

Effects of nursing team communication and collaboration on treatment outcomes in intensive care unit patients with severe pneumonia

BACKGROUND Severe pneumonia is a common severe respiratory infection worldwide,and its treatment is challenging,especially for patients in the intensive care unit(ICU).AIM To explore the effect of communication and collaboration between nursing teams on the treatment outcomes of patients with severe pneumonia in ICU.METHODS We retrospectively analyzed 60 patients with severe pneumonia who were treated at the ICU of the hospital between January 1,2021 and December 31,2023.We compared and analyzed the respiratory mechanical indexes[airway resistance(Raw),mean airway pressure(mPaw),peak pressure(PIP)],blood gas analysis indexes(arterial oxygen saturation,arterial oxygen partial pressure,and oxygenation index),and serum inflammatory factor levels[C-reactive protein(CRP),procalcitonin(PCT),cortisol(COR),and high mobility group protein B1(HMGB1)]of all patients before and after treatment.RESULTS Before treatment,there was no significant difference in respiratory mechanics index and blood gas analysis index between 2 groups(P>0.05).However,after treatment,the respiratory mechanical indexes of patients in both groups were significantly improved,and the improvement of Raw,mPaw,plateau pressure,PIP and other indexes in the combined group after communication and collaboration with the nursing team was significantly better than that in the single care group(P<0.05).The serum CRP and PCT levels of patients were significantly decreased,and the difference was statistically significant compared with that of nursing group alone(P<0.05).The levels of serum COR and HMGB1 before and after treatment were also significantly decreased between the two groups.CONCLUSION The communication and collaboration of the nursing team have a significant positive impact on respiratory mechanics indicators,blood gas analysis indicators and serum inflammatory factor levels in the treatment of severe pneumonia patients in ICU.

Intensive care unit-acquired weakness–preventive,and therapeutic aspects;future directions and special focus on lung transplantation

In this editorial,comments are made on an interesting article in the recent issue of the World Journal of Clinical Cases by Wang and Long.The authors describe the use of neural network model to identify risk factors for the development of intensive care unit(ICU)-acquired weakness.This condition has now become common with an increasing number of patients treated in ICUs and continues to be a source of morbidity and mortality.Despite identification of certain risk factors and corrective measures thereof,lacunae still exist in our understanding of this clinical entity.Numerous possible pathogenetic mechanisms at a molecular level have been described and these continue to be increasing.The amount of retrievable data for analysis from the ICU patients for study can be huge and enormous.Machine learning techniques to identify patterns in vast amounts of data are well known and may well provide pointers to bridge the knowledge gap in this condition.This editorial discusses the current knowledge of the condition including pathogenesis,diagnosis,risk factors,preventive measures,and therapy.Furthermore,it looks specifically at ICU acquired weakness in recipients of lung transplantation,because–unlike other solid organ transplants-muscular strength plays a vital role in the preservation and survival of the transplanted lung.Lungs differ from other solid organ transplants in that the proper function of the allograft is dependent on muscle function.Muscular weakness especially diaphragmatic weakness may lead to prolonged ventilation which has deleterious effects on the transplanted lung–ranging from ventilator associated pneumonia to bronchial anastomotic complications due to prolonged positive pressure on the anastomosis.

Advancing critical care recovery:The pivotal role of machine learning in early detection of intensive care unit-acquired weakness

This editorial explores the significant challenge of intensive care unit-acquiredweakness(ICU-AW),a prevalent condition affecting critically ill patients,characterizedby profound muscle weakness and complicating patient recovery.Highlightingthe paradox of modern medical advances,it emphasizes the urgent needfor early identification and intervention to mitigate ICU-AW's impact.Innovatively,the study by Wang et al is showcased for employing a multilayer perceptronneural network model,achieving high accuracy in predicting ICU-AWrisk.This advancement underscores the potential of neural network models inenhancing patient care but also calls for continued research to address limitationsand improve model applicability.The editorial advocates for the developmentand validation of sophisticated predictive tools,aiming for personalized carestrategies to reduce ICU-AW incidence and severity,ultimately improving patientoutcomes in critical care settings.

Development and validation of a nomogram for predicting in-hospital mortality of intensive care unit patients with liver cirrhosis

BACKGROUND Liver cirrhosis patients admitted to intensive care unit(ICU)have a high mortality rate.AIM To establish and validate a nomogram for predicting in-hospital mortality of ICU patients with liver cirrhosis.METHODS We extracted demographic,etiological,vital sign,laboratory test,comorbidity,complication,treatment,and severity score data of liver cirrhosis patients from the Medical Information Mart for Intensive Care IV(MIMIC-IV)and electronic ICU(eICU)collaborative research database(eICU-CRD).Predictor selection and model building were based on the MIMIC-IV dataset.The variables selected through least absolute shrinkage and selection operator analysis were further screened through multivariate regression analysis to obtain final predictors.The final predictors were included in the multivariate logistic regression model,which was used to construct a nomogram.Finally,we conducted external validation using the eICU-CRD.The area under the receiver operating characteristic curve(AUC),decision curve,and calibration curve were used to assess the efficacy of the models.RESULTS Risk factors,including the mean respiratory rate,mean systolic blood pressure,mean heart rate,white blood cells,international normalized ratio,total bilirubin,age,invasive ventilation,vasopressor use,maximum stage of acute kidney injury,and sequential organ failure assessment score,were included in the multivariate logistic regression.The model achieved AUCs of 0.864 and 0.808 in the MIMIC-IV and eICU-CRD databases,respectively.The calibration curve also confirmed the predictive ability of the model,while the decision curve confirmed its clinical value.CONCLUSION The nomogram has high accuracy in predicting in-hospital mortality.Improving the included predictors may help improve the prognosis of patients.

Effect of programmed comprehensive nursing on delirium incidence in intensive care unit children following severe cardiac surgery

BACKGROUND Programmed comprehensive nursing was adopted for intensive care unit(ICU)children following severe cardiac surgery to improve respiratory function and delirium incidence.AIM To explore how programmed comprehensive nursing impacts respiratory func-tion and delirium incidence in ICU children post cardiac surgery.METHODS Between January 2022 and January 2024,180 pediatric patients from the Chil-dren’s Hospital of Nanjing were admitted to the ICU after cardiac surgery and randomly grouped.The control group comprised 90 patients and received routine nursing care.The observation group comprised 90 patients and received program-med comprehensive nursing.Both groups received continuous nursing care until discharge.Their respiratory function,incidence of delirium,and clinical outcomes were compared.The memory state and sleep quality of both groups were com-pared.RESULTS The incidence of delirium was 5.56%in the observation group when admitted to ICU,which was lower than that in the control group(20.00%;P<0.05).The observation group demonstrated higher peak expiratory flow rate,respiratory frequency,deep breathing volume,and tidal volume in the ICU compared with the control group.Additionally,the observation group showed higher sleep depth,sleep latency,night awakening,return to sleep,and sleep quality com-pared with the control group(P<0.05).CONCLUSION Programmed comprehensive nursing in ICU patients following severe cardiac surgery can reduce the impact on respiratory function,improve sleep quality,and alleviate postoperative delirium,showing significant promise for clinical application.

Comparison of fungal vs bacterial infections in the medical intensive liver unit:Cause or corollary for high mortality?

BACKGROUND Due to development of an immune-dysregulated phenotype,advanced liver disease in all forms predisposes patients to sepsis acquisition,including by opportunistic pathogens such as fungi.Little data exists on fungal infection within a medical intensive liver unit(MILU),particularly in relation to acute on chronic liver failure.AIM To investigate the impact of fungal infections among critically ill patients with advanced liver disease,and compare outcomes to those of patients with bacterial infections.METHODS From our prospective registry of MILU patients from 2018-2022,we included 27 patients with culture-positive fungal infections and 183 with bacterial infections.We compared outcomes between patients admitted to the MILU with fungal infections to bacterial counterparts.Data was extracted through chart review.RESULTS All fungal infections were due to Candida species,and were most frequently blood isolates.Mortality among patients with fungal infections was significantly worse relative to the bacterial cohort(93%vs 52%,P<0.001).The majority of the fungal cohort developed grade 2 or 3 acute on chronic liver failure(ACLF)(90%vs 64%,P=0.02).Patients in the fungal cohort had increased use of vasopressors(96%vs 70%,P=0.04),mechanical ventilation(96%vs 65%,P<0.001),and dialysis due to acute kidney injury(78%vs 52%,P=0.014).On MILU admission,the fungal cohort had significantly higher Acute Physiology and Chronic Health Evaluation(108 vs 91,P=0.003),Acute Physiology Score(86 vs 65,P=0.003),and Model for End-Stage Liver Disease-Sodium scores(86 vs 65,P=0.041).There was no significant difference in the rate of central line use preceding culture(52%vs 40%,P=0.2).Patients with fungal infection had higher rate of transplant hold placement,and lower rates of transplant;however,differences did not achieve statistical significance.CONCLUSION Mortality was worse among patients with fungal infections,likely attributable to severe ACLF development.Prospective studies examining empiric antifungals in severe ACLF and associations between fungal infections and transplant outcomes are critical.

Improvement of the nutritional support management system for patients in intensive care units

BACKGROUND Nutritional support for patients hospitalized in the intensive care unit(ICU)is an important part of clinical treatment and care,but there are significant implementation difficulties.AIM To introduce a modified nutritional support management system for ICU patients based on closed-loop information management and psychological counseling.METHODS The division of functions,personnel training,system construction,development of an intelligent decision-making software system,quality control,and improvement of the whole process were carried out to systematically manage nutritional support for ICU patients.RESULTS Following the implementation of the whole process management system,the scores of ICU medical staff’s knowledge,attitudes/beliefs,and practices regarding nutritional support were comprehensively enhanced.The proportion of hospital bed-days of total enteral nutrition(EN)in ICU patients increased from 5.58%to 11.46%,and the proportion of EN plus parenteral nutrition increased from 42.71%to 47.07%.The rate of EN initiation within 48 h of ICU admission increased from 37.50%to 48.28%,and the EN compliance rate within 72 h elevated from 20.59%to 31.72%.After the implementation of the project,the Self-rating Anxiety Scale score decreased from 61.07±9.91 points to 52.03±9.02 points,the Self-rating Depression Scale score reduced from 62.47±10.50 points to 56.34±9.83 points,and the ICU stay decreased from 5.76±2.77 d to 5.10±2.12 d.CONCLUSION The nutritional support management system based on closed-loop information management and psychological counseling achieved remarkable results in clinical applications in ICU patients.

Effects of intensive psychological intervention on treatment compliance,psychological status,and quality of life of patients with epilepsy

BACKGROUND Epilepsy is a nervous system disease characterized by recurrent attacks,a long disease course,and an unfavorable prognosis.It is associated with an enduring therapeutic process,and finding a cure has been difficult.Patients with epilepsy are predisposed to adverse moods,such as resistance,anxiety,nervousness,and anxiety,which compromise treatment compliance and overall efficacy.AIM To explored the influence of intensive psychological intervention on treatment compliance,psychological status,and quality of life(QOL)of patients with epilepsy.METHODS The clinical data of 105 patients with epilepsy admitted between December 2019 and July 2023 were retrospectively analyzed,including those of 50 patients who underwent routine intervention(control group)and 55 who underwent intensive psychological intervention(research group).Treatment compliance,psychological status based on the Self-Rating Anxiety Scale(SAS)and Depression Scale Self-Rating Depression Scale(SDS)scores,hope level assessed using the Herth Hope Scale(HHS),psychological resilience evaluated using the Psychological Resilience Scale,and QOL determined using the QOL in Epilepsy-31 Inventory(QOLIE-31)were comparatively analyzed.RESULTS Treatment compliance in the research group was 85.5%,which is significantly better than the 68.0%of the control group.No notable intergroup differences in preinterventional SAS and SDS scores were identified(P>0.05);however,after the intervention,the SAS and SDS scores decreased significantly in the two groups,especially in the research group(P<0.05).The two groups also exhibited no significant differences in preinterventional HHS,Connor-Davidson Resilience Scale(CD-RISC),and QOLIE-31 scores(P>0.05).After 6 months of intervention,the research group showed evidently higher HHS,CD-RISC,tenacity,optimism,strength,and QOLIE-31 scores(P<0.05).CONCLUSION Intensive psychological intervention enhances treatment compliance,psychological status,and QOL of patients with epilepsy.

Effects of strengthening prospective nursing practice on sleep quality,anxiety,and depression of awake patients in intensive care unit

BACKGROUND The intensive care unit(ICU)is a specialized hospital department.Awake patients in the ICU frequently encounter adverse psychological states,such as anxiety and fear,often accompanied by poor sleep quality.This situation has garnered significant attention within the medical community.AIM To investigate the impact of prospective nursing intervention strategies on the sleep quality and negative emotional state of conscious ICU patients.METHODS One hundred and twenty ICU awake patients admitted to our hospital were selected and randomly divided into control(n=60)and observation(n=60)groups.Patients in the control group were cared for using the conventional nursing model,while patients in the observation group were cared for using the prospective nursing model.Sleep improvement was assessed using the International Standardized Sleep Efficiency Formula and Pittsburgh Sleep Quality Index(PSQI).The PSQI,Generalized Anxiety Disorder 7-item(GAD-7)scale,Self-Depression Scale(SDS),and satisfaction before and after treatment were used to assess the negative emotional states of patients under the two care models.RESULTS Patient satisfaction in the observation group was significantly higher than in the control group.The GAD-7 and SDS scores in the observation group were significantly lower than those in the control group,and the total effective rate of sleep improvement in the observation group was significantly higher than in the control group.After treatment,the PSQI scores of the two groups significantly decreased(P<0.05).The decrease in the observation group was more significan than that in the control group,and the difference between the two groups was statistically significant.CONCLUSION Prospective nursing interventions can improve sleep quality and psychological levels and significantly affect conscious patients in the ICU,which is worthy of clinical application.