BACKGROUND: Hospitalization in neonatal intensive care units (NICU) exposes preterm infants to adverse stimuli, including continuous 24-hour lighting. There is currently no standardized NICU layout advised for the best development of preterm neonates. This meta-analysis aimed to assess the impact of cycled light (CL) exposure on clinical outcomes in premature infants admitted to NICU as synthesized in previous studies. MATERIALS AND METHODS: This meta-analysis protocol was developed following the preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) statement. A search was performed in PubMed/MEDLINE, EMBASE, Scopus, and Cochrane databases using the MeSH/key words: ―light exposure‖ AND pre-term AND cycled AND (RCT OR trials OR ―randomized controlled trial). The pooled Mean Difference with corresponding 95% CI was computed for weight gain, duration until start of enteral feeding, and duration of ICU stay using the Mantel–Haenszel random-effect model. RESULTS: Nine studies were included. The pooled mean difference showed that among preterm infants who had cycled light exposure, average daily weight gain (MD=6.24 grams, 95%CI=1.36 to 11.13, p=0.01) was significantly higher than those with continuous light exposure. The average time to start enteral feeding (MD=-3.84 days, 95%CI=-7.56 to -0.13, p=0.04) and average ICU stay (MD=-8.43 days, 95%CI=-12.54 to -4.31, p<0.0001) among neonates who had cycled light exposure were significantly shorter. CONCLUSION Benefits were seen in preterm infants when exposed to cycled light as opposed to continuous light. CL exposed infants showed a daily weight gain that was 6.24 grams higher, on average, and began enteral feeding nearly 4 days sooner. It led to a decrease in the duration of ICU stay by around 8 to 9 days on average. Further trials to determine the impact of cycled light exposure on morbidity and mortality among preterm neonates is recommended.
Human ; Male,Female ; Systematic review ; Meta-analysis ; Infant, Premature ; Intensive care units, Neonatal ; Intensive care, Neonatal ; Light ; Lighting ; Critical care

PURPOSE: Septic shock is associated with high mortality and poor outcomes among sepsis patients with coagulopathy. Although traditional statistical methods or machine learning (ML) algorithms have been proposed to predict septic shock, these potential approaches have never been systematically compared. The present work aimed to develop and compare models to predict septic shock among patients with sepsis. METHODS: It is a retrospective cohort study based on 484 patients with sepsis who were admitted to our intensive care units between May 2018 and November 2022. Patients from the 908th Hospital of Chinese PLA Logistical Support Force and Nanchang Hongdu Hospital of Traditional Chinese Medicine were respectively allocated to training (n=311) and validation (n=173) sets. All clinical and laboratory data of sepsis patients characterized by comprehensive coagulation indexes were collected. We developed 5 models based on ML algorithms and 1 model based on a traditional statistical method to predict septic shock in the training cohort. The performance of all models was assessed using the area under the receiver operating characteristic curve and calibration plots. Decision curve analysis was used to evaluate the net benefit of the models. The validation set was applied to verify the predictive accuracy of the models. This study also used Shapley additive explanations method to assess variable importance and explain the prediction made by a ML algorithm. RESULTS: Among all patients, 37.2% experienced septic shock. The characteristic curves of the 6 models ranged from 0.833 to 0.962 and 0.630 to 0.744 in the training and validation sets, respectively. The model with the best prediction performance was based on the support vector machine (SVM) algorithm, which was constructed by age, tissue plasminogen activator-inhibitor complex, prothrombin time, international normalized ratio, white blood cells, and platelet counts. The SVM model showed good calibration and discrimination and a greater net benefit in decision curve analysis. CONCLUSION The SVM algorithm may be superior to other ML and traditional statistical algorithms for predicting septic shock. Physicians can better understand the reliability of the predictive model by Shapley additive explanations value analysis.
Humans ; Shock, Septic/blood* ; Machine Learning ; Male ; Female ; Retrospective Studies ; Middle Aged ; Aged ; Sepsis/complications* ; ROC Curve ; Cohort Studies ; Adult ; Intensive Care Units ; Algorithms ; Blood Coagulation ; Critical Illness

OBJECTIVES: To investigate the association of serum albumin level after human albumin infusion with 28-day mortality in critically ill patients with acute kidney injury (AKI) and its impact on 90-day outcomes of the patients. METHODS: We conducted a retrospective cohort study based on the MIMIC IV database (2008-2019), including 5918 AKI patients treated with albumin in the ICU. Based on serum albumin levels within 72 h after albumin infusion, the patients were divided into low (<30 g/L), medium (30-35 g/L), and high albumin (>35 g/L) groups. Restricted cubic spline regression and multivariate logistic regression were used to analyze the association of albumin levels with patient mortality, and the results were verified in a external validation cohort consisting of 110 sepsis-induced AKI patients treated in Nanfang Hospital between 2017 and 2022 using survival analysis and multivariate adjustment. RESULTS: In the MIMIC training cohort, multivariate logistic regression showed no significant differences in 28-day mortality of the patients with different albumin levels (P>0.05). However, restricted cubic spline analysis indicated a non-linear dose-response relationship between albumin levels and 28-day mortality (threshold effect: risk increased when albumin levels >3.6 g/dL). Secondary endpoint analysis revealed that the patients with high albumin levels had a shorter duration of mechanical ventilation (P<0.001) but a longer ICU stay (P<0.001). In the validation cohort, albumin levels ≥30 g/L were significantly associated with a reduced 28-day mortality rate (P<0.05). CONCLUSIONS The association between increased serum albumin levels following albumin infusion and 28-day mortality of critically ill patients with AKI exhibits a cohort dependency and can be influenced by multiple factors including disease type and severity, infusion strategies, and statistical methods.
Humans ; Acute Kidney Injury/therapy* ; Critical Illness/mortality* ; Retrospective Studies ; Serum Albumin/analysis* ; Male ; Female ; Intensive Care Units ; Middle Aged ; Logistic Models ; Aged

OBJECTIVE: To explore the feasibility of incorporating simple bedside indicators into death predictive model for elderly critically ill patients based on interpretability machine learning algorithms, providing a new scheme for clinical disease assessment. METHODS: Elderly critically ill patients aged ≥ 65 years who were hospitalized in the intensive care unit (ICU) of Tacheng People's Hospital of Ili Kazak Autonomous Prefecture from June 2017 to May 2020 were retrospectively selected. Basic parameters including demographic characteristics, basic vital signs and fluid intake and output within 24 hours after admission, as well acute physiology and chronic health evaluation II (APACHE II), Glasgow coma score (GCS) and sequential organ failure assessment (SOFA) were also collected. According to outcomes in hospital, patients were divided into survival group and death group. Four datasets were constructed respectively, namely baseline dataset (B), including age, body temperature, heart rate, pulse oxygen saturation, respiratory rate, mean arterial pressure, urine output volume, infusion volume, and crystal solution volume; B+APACHE II dataset (BA), B+GCS dataset (BG), and B+SOFA dataset (BS). Then three machine learning algorithms, Logistic regression (LR), extreme gradient boosting (XGboost) and gradient boosting decision tree (GBDT) were used to develop the corresponding mortality predictive models within four datasets. The feature importance histogram of each prediction model was drawn by SHapley additive explanation (SHAP) method. The area under curve (AUC), accuracy and F1 score of each model were compared to determine the optimal prediction model and then illuminate the nomogram. RESULTS: A total of 392 patients were collected, including 341 in the survival group and 51 in the death group. There were statistically significant differences in heart rate, pulse oxygen saturation, mean arterial pressure, infusion volume, crystal solution volume, and etiological distribution between the two groups. The top three causes of death were shock, cerebral hemorrhage, and chronic obstructive pulmonary disease. Among the 12 prognostic models trained by three machine learning algorithms, overall performance of prognostic models based on B dataset was behind, whereas the LR model trained by BA dataset achieved the best performance than others with AUC of 0.767 [95% confidence interval (95%CI) was 0.692-0.836], accuracy of 0.875 (95%CI was 0.837-0.903) and F1 score of 0.190. The top 3 variables in this model were crystal solution volume with first 24 hours, heart rate and mean arterial pressure. The nomogram of the model showed that the total score between 150 and 230 were advisable. CONCLUSION The interpretable machine learning model including simple bedside parameters combined with APACHE II score could effectively identify the risk of death in elderly patients with critically illness.
Humans ; Critical Illness ; Machine Learning ; Aged ; Algorithms ; Intensive Care Units ; Retrospective Studies ; APACHE ; Prognosis ; Organ Dysfunction Scores ; Hospital Mortality ; Male ; Female

OBJECTIVE: To observe the effects of enhanced rehabilitation on the prognosis of critically ill patients in the intensive care unit (ICU). METHODS: A single-center retrospective historical controlled study was conducted, patients admitted to the ICU of Peking University First Hospital from May 1, 2020, to April 30, 2021, and from October 1, 2021, to September 30, 2022 were enrolled. According to the different rehabilitation treatment strategies during different periods, patients were divided into the conventional rehabilitation group (patients receiving conventional rehabilitation treatment from May 1, 2020, to April 30, 2021) and the enhanced rehabilitation group (patients receiving the therapy of multidisciplinary team, ie medical care-rehabilitation-nursing care from October 1, 2021, to September 30, 2022). General data, acute physiology and chronic health evaluation II (APACHE II), and study endpoints were collected. Primary endpoints included rehabilitation-therapy rate, intervention time for rehabilitation, rehabilitation-related adverse events, and prognostic indicators such as (length of stay in hospital, length of stay in the ICU, and duration of mechanical ventilation). Secondary endpoints included incidence of deep vein thrombosis and hospital mortality. Kaplan-Meier curves were used to analyze cumulative discharge rates within 50 days. RESULTS: A total of 539 ICU patients were enrolled, with 245 in the conventional rehabilitation group and 294 in the enhanced rehabilitation group; 322 patients had an APACHE II score ≤ 15, while 217 patients had an APACHE II score > 15. Compared to the conventional rehabilitation group, the enhanced rehabilitation group demonstrated significantly higher rehabilitation-therapy rate [51.70% (152/294) vs. 11.43% (28/245)], earlier intervention time for rehabilitation [days: 2.00 (1.00, 3.00) vs. 4.00 (3.00, 7.00)]; shorter length of stay in hospital [days: 18.00 (12.00, 30.00) vs. 21.00 (13.00, 36.00)] and lower incidence of DVT [17.01% (50/294) vs. 24.08% (59/245)]. The differences were all statistically significant (all P < 0.05). There were no rehabilitation-related adverse events occurred in either group. Kaplan-Meier analysis demonstrated a significantly higher cumulative discharge rate within 50 days in the enhanced rehabilitation group compared to the conventional rehabilitation group [86.7% (255/294) vs. 82.9% (203/245); Log-Rank test: χ² = 4.262, P = 0.039]. Subgroup analysis showed that for patients with APACHE II score ≤ 15, the enhanced rehabilitation subgroup had higher rehabilitation-therapy rate [44.32% (78/176) vs. 6.16% (9/146), P < 0.05]. For patients with APACHE II score > 15, compared to the conventional rehabilitation group, the enhanced subgroup demonstrated higher rehabilitation-therapy rate [62.71% (74/118) vs. 19.19% (19/99), P < 0.05] and shorter length of stay in hospital [days: 20.50 (12.00, 31.25) vs. 26.00 (16.00, 43.00), P < 0.05]. CONCLUSIONS Enhanced rehabilitation therapy with medical care, rehabilitation and nursing care, improved rehabilitation-therapy rate, advanced time of rehabilitation treatment, reduced length of stay in hospital and incidence of deep vein thrombosis in critically ill patients, particularly benefited those with APACHE II score > 15. The enhanced rehabilitation was beneficial to the patient in the intensive care unit with safety and worth more investigation.
Humans ; Retrospective Studies ; Critical Illness/rehabilitation* ; Intensive Care Units ; Prognosis ; Length of Stay ; APACHE ; Historically Controlled Study ; Male ; Female ; Middle Aged ; Aged

OBJECTIVE: To evaluate the impact of critical care warning platform (CWP) on clinical outcomes of patients transferred from internal medical ward to intensive care unit (ICU) based on real-world data. METHODS: A retrospective cohort study was conducted. The patients transferred from internal medical ward to ICU of Zhongda Hospital, Southeast University, between January 2022 and October 2024, were enrolled. They were divided into critical care warning group and conventional treatment group based on whether they were connected to the CWP. The patients in the critical care warning group were connected to the CWP, which collected real-time vital signs and treatment data. The platform automatically calculated severity scores, generated individualized risk assessments, and triggered warning alerts, allowing clinicians to adjust treatment plans accordingly. The patients in the conventional treatment group were not connected to the CWP and relied on conventional clinical judgment and nursing measures for treatment management. Baseline characteristics [gender, age, body mass index (BMI), admission type, severity score of illness, underlying diseases, and disease type at ICU admission], primary clinical outcome (in-hospital mortality), and secondary clinical outcomes [ICU mortality, length of ICU stay, total length of hospital stay, and mechanical ventilation and continuous renal replacement therapy (CRRT) status] were collected. Multivariate Logistic regression was used to analyze the impact of CWP on in-hospital death, and subgroup analyses were performed based on different patient characteristics. RESULTS: A total of 1 281 patients were enrolled, with 768 in the critical care warning group and 513 in the conventional treatment group. Compared with the conventional treatment group, the proportion of patients in the critical care warning group with underlying diseases of diabetes and malignancy and transferred to ICU due to sepsis was lowered, however, there were no statistically significant differences in other baseline characteristics between the two groups. Regarding the primary clinical outcome, the in-hospital mortality in the critical care warning group was significantly lower than that in the conventional treatment group [17.6% (135/768) vs. 25.7% (132/513), P < 0.01]. For secondary clinical outcomes, compared with the conventional treatment group, the patients in the critical care warning group had significantly fewer days of mechanical ventilation within 28 days [days: 2 (1, 6) vs. 2 (1, 8), P < 0.05], significantly shorter length of ICU stay [days: 3 (2, 8) vs. 4 (2, 10), P < 0.01], and significantly lower ICU mortality [15.1% (116/768) vs. 21.4% (110/513), P < 0.01]. Multivariate Logistic regression analysis showed that, after adjusting for age and underlying diseases, the use of CWP was significantly associated with a reduction of in-hospital mortality among patients transferred from internal medical ward to ICU [odds ratio (OR) = 0.670, 95% confidence interval (95%CI) was 0.502-0.894, P = 0.006]. Further subgroup analysis revealed that, among patients transferred to ICU due to sepsis, the use of CWP significantly reduced in-hospital mortality (OR = 0.514, 95%CI was 0.367-0.722, P < 0.001). In patients aged ≥ 70 years old (OR = 0.587, 95%CI was 0.415-0.831, P = 0.003) and those with underlying diseases of malignancy (OR = 0.124, 95%CI was 0.046-0.330, P < 0.001), CWP also showed significant protective effects on in-hospital prognosis. CONCLUSION The use of CWP is significantly associated with a reduction in in-hospital mortality among patients transferred from internal medical ward to ICU, demonstrating its potential in assessing the deterioration of hospitalized patients.
Humans ; Intensive Care Units ; Retrospective Studies ; Hospital Mortality ; Prognosis ; Critical Care ; Male ; Female ; Patient Transfer ; Middle Aged ; Aged ; Cohort Studies

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

Critical care emphasizes critical thinking, focuses on the triggers that lead to disease progression, and attaches great importance to early diagnosis of diseases and assessment of the compensatory capacity of vital organs. Pregnancy-associated atypical hemolytic uremic syndrome (P-aHUS) is relatively rare in the intensive care unit (ICU). Most cases occur within 10 weeks after delivery. Severe cases can be life-threatening. It characterized by microangiopathic hemolytic anemia, decreased platelet count (PLT), and acute kidney injury (AKI). Early clinical diagnosis is difficult due to its similarity to various disease manifestations. On January 28, 2024, a 26-year-old pregnant woman at 26⁺³ weeks gestation was transferred to the ICU 19 hours post-vaginal delivery due to abdominal pain, reduced urine output, decreased PLT, elevated D-dimer, tachycardia, increased respiratory rate and declined oxygenation. On the day of ICU admission, the critical care physician identified the causes that triggered the acute respiratory and circulatory events based on the "holistic and local" critical care thinking. The condition was stabilized rapidly by improving the capacity overload. In terms of etiological diagnosis, under the guidance of the "point and face" critical care thinking, starting from abnormality indicators including a decrease in hemoglobin (Hb) and PLT and elevated D-dimer and fibrin degradation product (FDP) without other abnormal coagulation indicators, the critical care physician ultimately determined the diagnosis direction of thrombotic microangiopathy (TMA) by delving deeply into the essence of the disease and formulating a laboratory examination plan in a reasonable and orderly manner. In terms of in-depth diagnosis, combining the disease development process, family history, and past history, applying the two-way falsification thinking of "forward and reverse" as well as "questioning and hypothesis", the diagnosis possibilities of preeclampsia, HELLP syndrome [including hemolysis (H), elevated liver function (EL) and low platelet count (LP)], thrombotic thrombocytopenic purpura (TTP), typical hemolytic uremic syndrome (HUS), and autoimmune inflammatory diseases inducing the condition was ruled out. The diagnosis of complement activation-induced P-aHUS was finally established for the patient, according to the positive result of the complement factor H (CFH). Active decision was made in the initial treatment. The plasma exchange was initiated early. "Small goals" were formulated in stages. The "small endpoints" were dynamically controlled in a goal-oriented manner to achieve continuous realization of the overall treatment effect through phased "small goals". On the 5th day of ICU treatment, the trend of microthrombosis in the patient was controlled, organ function damage was improved, and the patient was transferred out of the ICU. It is possible to reach a favorable clinical outcome for critically ill patients by applying a critical care mindset to quickly integrate diagnostic and therapeutic strategies, accurately identifying the triggers and causes that led to the progression of the disease, and using critical care medical techniques for early and effective intervention.
Humans ; Female ; Pregnancy ; Adult ; Atypical Hemolytic Uremic Syndrome/therapy* ; Intensive Care Units ; Pregnancy Complications, Hematologic/therapy* ; Critical Care

OBJECTIVE: To investigate the current status of early pain and agitation management in critically ill patients in Guizhou Province. METHODS: A retrospective study was performed using data collected from a quality control activity conducted between April and June 2021 in non-provincial public hospitals with general intensive care unit (ICU) in Guizhou Province. Hospital-level data included hospital name and grade, ICU staffing, and number of ICU beds. Patient-level data included characteristics of patients treated in the general ICU on the day of the survey (e.g., age, sex, primary diagnosis), as well as pain and agitation assessments and the types of analgesic and sedative medications administered within 24 hours of ICU admission. RESULTS: A total of 947 critically ill ICU patients from 145 hospitals were included, among which 104 were secondary-level hospitals and 41 were tertiary-level hospitals. Within 24 hours of ICU admission, 312 (32.9%) critically ill patients received pain assessments, and 277 (29.3%) received agitation assessments. Among the pain assessment tools, the critical care pain observation tool (CPOT) was used in 44.2% (138/312) of critically ill ICU patients, with a significantly higher usage rate in tertiary hospitals compared to secondary hospitals [52.3% (69/132) vs. 38.3% (69/180), P < 0.05]. The Richmond agitation-sedation scale (RASS) was used in 93.8% (260/277) of critically ill ICU patients for agitation assessment, with no significant difference between hospital levels. Among the 947 critically ill patients, 592 (62.5%) received intravenous analgesics within 24 hours, with remifentanil being the most commonly used [42.9% (254/592)]; 510 (53.9%) received intravenous sedatives, with midazolam being the most frequently used [60.8% (310/510)]. Mechanical ventilation data were available for 932 critically ill patients, of whom 579 (62.1%) received mechanical ventilation and 353 (37.9%) did not. Compared with non-ventilated patients, ventilated patients had significantly higher rates of analgesic and sedative use [analgesics: 77.9% (451/579) vs. 38.8% (137/353); sedatives: 71.8% (416/579) vs. 25.8% (91/353); both P < 0.05]. In terms of analgesic selection, ventilated patients were more likely to receive strong opioids than non-ventilated patients [85.8% (95/137) vs. 69.3% (387/451), P < 0.05]. For sedatives, ventilated patients preferred midazolam [66.6% (277/416)], whereas non-ventilated patients more often received dexmedetomidine [45.1 (41/91)]. Blood pressure within 24 hours of ICU admission were available for 822 critically ill patients, of whom 245 (29.8%) had hypotension and 577 (70.2%) did not. Compared with non-hypotensive patients, hypotensive patients had significantly higher rates of analgesic and sedative use [analgesics: 74.7% (183/245) vs. 59.8% (345/577); sedatives: 65.7% (161/245) vs. 51.3% (296/577); both P < 0.05], but there was no significant difference in the choice of analgesic or sedative agents between the two groups. CONCLUSIONS The proportion of critically ill ICU patients in Guizhou Province who received standardized pain and agitation assessments was relatively low. The most commonly used assessment tools were CPOT and RASS, while remifentanil and midazolam were the most frequently used analgesic and sedative agents, respectively. Secondary-level hospitals had a lower rate of using standardized pain assessment tools compared to tertiary-level hospitals. Mechanical ventilation and hypotension were associated with the use of analgesic and sedative medications.
Humans ; Critical Illness ; Intensive Care Units ; Analgesics/therapeutic use* ; Hypnotics and Sedatives/therapeutic use* ; Retrospective Studies ; China ; Pain Measurement ; Pain Management ; Female ; Male ; Critical Care ; Middle Aged

OBJECTIVE: To explore the effect of light music therapy on delirium in intensive care unit (ICU) patients undergoing invasive mechanical ventilation, and provide evidence-based support for clinical prevention of delirium. METHODS: A prospective randomized controlled trial was conducted. 140 patients with invasive mechanical ventilation admitted to the department of respiratory and critical care medicine of First Affiliated Hospital of Guangxi Medical University from January 2024 to January 2025 were enrolled. The patients were divided into intervention group and control group using a random number table method. The control group received routine treatment and nursing care, while the intervention group received light music therapy three times a day for 30 minutes each time for 7 consecutive days. The confusion assessment method-ICU (CAM-ICU) was used to evaluate delirium, and the incidence of delirium within 7 days was statistically analyzed. Richmond agitation-sedation score (RASS), critical care pain observation tool (CPOT) score, mechanical ventilation duration, the length of ICU stay, and ICU stay expenses were record. RESULTS: 129 cases were ultimately included, including 64 cases in the control group and 65 cases in the intervention group. There was no statistically significant difference in baseline data between the two groups, indicating comparability. The incidence of delirium in the intervention group was significantly lower than that in the control group (27.7% vs. 51.6%, χ ² = 7.687, P = 0.006). There was no significantly difference in RASS score between the two groups before enrollment (P = 0.840). After intervention, the RASS score in the intervention group significantly decreased, from 2.00 points on the 1st day of enrollment to 0.00 points on the 7th day, while the control group only decreased from 2.00 points to 1.50 points. The decreasing trend of the intervention group was more pronounced, especially on the 3rd day (P = 0.047) and the 7th day (P =0.005), with significant differences between the groups. The time effect (F = 18.929, P < 0.001), group effect (F = 6.655, P = 0.011), and time group interaction effect (F = 7.372, P < 0.001) of the two groups of RASS score were significant, suggesting that light music therapy has better timeliness and sustainability in improving patients' sedation status. There was no significantly difference in CPOT score between the two groups before enrollment (P = 0.902). After intervention, the CPOT score in the intervention group rapidly decreased from 3.00 points before enrollment to 1.00 points on the 1st day, and continued until the 7th day, while the control group showed a slower decrease from 2.50 points to 2.00 points and only dropped to 1.00 points on the 7th day. There were significant differences on 1st day and 3rd day between two groups (both P < 0.05). The time effect (F = 28.125, P < 0.001), group effect (F = 11.580, P = 0.001), and time group interaction effect (F = 4.048, P = 0.020) of the two groups of CPOT score were significant, indicating that light music therapy has better pain control, but the interaction effect is low, indicating that the impact of the intervention on the CPOT score was mainly concentrated in the early stage (1-3 days), and the long-term effect may be influenced by other factors. Compared with the control group, the intervention group showed a significant reduction in mechanical ventilation time (days: 10.57±2.94 vs. 11.95±3.74, P = 0.021) and the length of ICU stay (days: 14.91±4.37 vs. 17.53±4.83, P = 0.002). The ICU hospitalization expenses of the intervention group was slightly lower than that of the control group [ten thousand yuan: 22.431 (12.473, 28.489) vs. 29.362 (11.996, 41.389)], but the difference was not statistically significant (P = 0.086). CONCLUSIONS Light music therapy can effectively reduce the incidence of delirium in patients undergoing invasive mechanical ventilation, improve consciousness and pain perception, shorten mechanical ventilation time and hospital stay, and has significant clinical promotion value high-quality studies.
Humans ; Delirium/prevention & control* ; Intensive Care Units ; Respiration, Artificial ; Music Therapy ; Prospective Studies ; Male ; Female ; Middle Aged ; Critical Care ; Aged