OBJECTIVE: To evaluate the effect of palliative care on drug use, medical service utilization and medical expenditure of patients with advanced cancer. METHODS: A cohort of patients including pal-liative care and standard care was constructed using the medical records of the patients in Peking University Cancer Hospital from 2018 to 2020, and coarsened exact matching was used to match the two groups of patients. The average monthly opioid consumption, hospitalization rate, intensive care unit (ICU) rate and operation rate, and the average monthly total cost were selected to evaluate drug use, medical service utilization and medical expenditure. Chi-square test and Wilcoxon signed rank test were used to compare the differences between the two groups before and after exposure and the change in the palliative care group. The net impact of palliative care on the patients was calculated using the difference-in-differences analysis. RESULTS: In this study, 180 patients in the palliative care group and 3 101 patients in the stan-dard care group were finally included in the matching, and the matching effect of the two groups was good (L₁ < 0.1). Before and after exposure, the average monthly opioid consumption in the palliative care group was significantly higher than that in the standard care group (Before exposure: 0.3 DDD/person-month vs. 0.1 DDD/person-month, P < 0.01; After exposure: 0.7 DDD/person-month vs. 0.1 DDD/person-month, P < 0.01; DDD refers to defined daily dose), palliative care significantly increased the average monthly opioid consumption in the patients (0.3 DDD/person-month, P < 0.01). The hospitalization rate (48.9% vs. 74.3%, P < 0.01) and operation rate (3.9% vs. 8.8%, P < 0.01) of the patients in palliative care group were significantly lower than those in standard care group, and the ICU rate became similar between the two groups (1.1% vs. 1.6%, P=0.634). Palliative care significantly reduced the patients ' hospitalization rate (-25.6%, P < 0.01), ICU rate (-4.9%, P < 0.01) and operation rate (-14.5%, P < 0.01). Before and after exposure, the average monthly total costs of pal-liative care group were slightly higher than those of standard care group (Before exposure: 20 092.3 yuan vs. 19 132.8 yuan, P=0.725; After exposure: 9 719.8 yuan vs. 8 818.8 yuan, P=0.165). Palliative care increased the average monthly total cost by 2 208.8 yuan, but it was not statistically significant (P=0.316). CONCLUSION Palliative care can increase the opioid consumption in advanced cancer patients, reduce the rates of hospitalization, ICU and surgery, but has no significant effect on medical expenditure.
Humans ; Palliative Care/economics* ; Neoplasms/drug therapy* ; Analgesics, Opioid/economics* ; Male ; Female ; Middle Aged ; Aged ; Hospitalization/economics* ; Intensive Care Units/statistics & numerical data* ; Health Expenditures/statistics & numerical data* ; Adult ; Drug Utilization/statistics & numerical data* ; Patient Acceptance of Health Care/statistics & numerical data*

OBJECTIVE: To analyze the clinical characteristics, microbiological analysis, and drug resistance patterns of intensive care unit (ICU) bloodstream infection. METHODS: A prospective cohort study method was employed to collect clinical data from patients suspected of bloodstream infection (BSI) during their stay in ICUs across 67 hospitals in 16 provinces and cities nationwide, from July 1, 2021, to December 31, 2022. Electronic data collection technology was used to gather general information on ICU patients, including gender, age, length of hospital stay, as well as diagnostic results, laboratory tests, imaging studies, microbiological results (including smear, culture results, and pathogen high-throughput testing), and prognosis. Patients were divided into a BSI group and a non-BSI group based on the presence or absence of BSI; further, patients with BSI were categorized into a drug-resistant group and a non-drug-resistant group based on the presence or absence of drug resistance. Differences in the aforementioned indicators between groups were analyzed and compared; variables with P < 0.10 in the univariate analysis were included in a multivariate Logistic regression analysis to identify risk factors for mortality and drug resistance in ICU patients with BSI. RESULTS: A total of 2 962 ICU patients suspected of BSI participated in the study, including 790 in the BSI group and 2 172 in the non-BSI group. Patients in the BSI group were mainly from East China and Southwest China, with significantly higher age and mortality rates than those in the non-BSI group. Among ICU patients with BSI, Staphylococcus had the highest detection rate (8.10%), followed by Klebsiella pneumoniae (7.47%); there were 169 cases in the drug-resistant group and 621 cases in the non-drug-resistant group; 666 cases survived, and 124 cases died (mortality was 15.70%). There were statistically significant differences between the death group and the survival group in terms of age, regional distribution, and bloodstream infections caused by Gram negative (G^-) bacilli, Enterococcus faecium, Aspergillus, and Klebsiella pneumoniae; multivariate Logistic regression analysis showed that age [odds ratio (OR) = 1.01, 95% confidence interval (95%CI) was 1.00-1.03], regional distribution (OR = 4.07, 95%CI was 1.02-1.34), Enterococcus faecium infection (OR = 3.64, 95%CI was 1.16-11.45), and Klebsiella pneumoniae infection (OR = 2.64,95%CI was 1.45-4.80) were independent risk factors for death in ICU patients with BSI (all P < 0.05). There were statistically significant differences between the drug-resistant group and the non-drug-resistant group in terms of age and bloodstream infections caused by Gram positive (G⁺) cocci and G^- bacilli; multivariate Logistic regression analysis showed that age (OR = 1.01,95%CI was 1.00-1.03), G^- bacilli infection (OR = 2.18, 95%CI was 1.33-3.59), Escherichia coli infection (OR = 0.28,95%CI was 0.09-0.84), and Enterococcus faecium infection (OR = 3.35, 95%CI was 1.06-10.58) were independent risk factors for drug resistance in ICU patients with BSI (all P < 0.05). CONCLUSIONS Bloodstream infections may increase the mortality of ICU patients. Older age, regional distribution, Enterococcus faecium infection and Klebsiella pneumoniae infection can increase the mortality rate of ICU patients with BSI; bloodstream infections caused by G^- bacilli are prone to drug resistance, but have no significant impact on the mortality of ICU patients with BSI.
Adult ; Humans ; Bacteremia/microbiology* ; China/epidemiology* ; Cohort Studies ; Cross Infection/microbiology* ; Drug Resistance, Bacterial ; Intensive Care Units/statistics & numerical data* ; Prospective Studies ; Risk Factors ; Sepsis/microbiology*

INTRODUCTION: There are limited reports on the epidemiology of paediatric intensive care unit (PICU) admissions, deaths and organ donation candidacy. We aimed to describe PICU admission characteristics and outcomes, determine risk factors for mortality, and perform an independent assessment of missed organ donation opportunities. METHOD: We adopted a clinical audit design recruiting consecutive patients admitted to a single-centre multidisciplinary PICU from June 2020 to December 2023. Clinical characteristics and outcomes of survivors and non-survivors were described. Multivariable regression was performed to identify independent risk factors for mortality. Organ donation candidacy was evaluated by an independent team based on the criteria by Singapore's National Organ Transplant Unit. RESULTS: There were 1766 PICU admissions with mean age ± standard deviation of 5.9 ± 6.0 years. Surgical admissions accounted for 707/1766 (40%), while the most common medical admission category was respiratory (416/1766; 23.6%). The majority of 983/1766 (55.7%) had a chronic comorbidity and 312/1766 (17.6%) were dependent on at least 1 medical technology device. Mortality occurred in 99/1766 (5.6%). After adjusting for elective admissions and admission category; comorbidity with adjusted odds ratio (aOR) 95% confidence interval (CI) 3.03 (1.54-5.96); higher Pediatric Index of Mortality 3 (PIM 3) score with aOR 1.06 (95% CI 1.04-1.08); and functional status scale with aOR 1.07 (95% CI 1.00-1.13) were associated with mortality. Among non-survivors, organ donor candidacy was 21/99 (21.2%) but successful organ donation occurred in only 2/99 (2.0%). CONCLUSION In this single-centre audit, comorbidities, PIM 3 score and functional impairment were associated with mortality. Efforts are needed to improve paediatric organ donation rates.
Humans ; Male ; Female ; Tissue and Organ Procurement/statistics & numerical data* ; Intensive Care Units, Pediatric/statistics & numerical data* ; Child, Preschool ; Child ; Infant ; Singapore/epidemiology* ; Risk Factors ; Patient Admission/statistics & numerical data* ; Hospital Mortality ; Adolescent ; Medical Audit ; Comorbidity ; Clinical Audit

OBJECTIVE: To analyze the clinical characteristics of patients with severe fall injury and explore the prehospital factors affecting the injury severity score (ISS). METHODS: Clinical data of severe trauma patients with fall injury and ISS≥16 from January 2018 to December 2020 were retrieved from trauma database of Peking University People' s Hospital. The patients' age, gender, suicidal tendencies, psychiatric disorders, fall height, properties of the impact surface, the body part hitting the ground, abbreviated injury scale, Glasgow coma scale (GCS), length of stay in intensive care unit (ICU), operation were collected. And the in-hospital mortality were calculated. Univariate analysis and multiple linear regression models were used to analyze the relationship between the above factors and ISS. The patients' GCS, length of stay in ICU, surgery, and in-hospital mortality were collected to analyze the general clinical characteristics of patients. RESULTS: A total of 160 patients were finally eligible, including 138 males and 22 females, with an average age of (45.56±15.85) years. Among the 160 patients, there were 36 cases (22.50%) with suicidal tendencies, 12 cases (7.50%) with psychiatric disorders. Their average fall height was (7.20±8.33) meters, and 48 cases (30.00%) hit the soft contact medium. 40 cases (25.00%) with impact on the head at the ground, lower limbs in 26 cases (16.25%), ventral in 16 cases (10.00%), dorsal in 40 cases (25.00%), lateral in 38 cases (23.75%). The patients' ISS was 22.8±6.85, GCS was 13.49±3.39, lengths of ICU stays were (9.96±8.12) days, and 142 (88.75%) patients underwent surgery, 8 in-hospital deaths were all due to head trauma, with an in-hospital mortality rate of 5.00%. Univariate analysis suggested that the main factors influencing ISS were the presence of suicidal tendencies (P=0.01) and the site of impact on the ground (P=0.02). Multiple linear regression analysis indicated that suicidal tendencies and head impact on the ground were in-fluential factors for high ISS. CONCLUSION Collecting prehospital information of patients with fall injuries, such as whether they have suicidal tendencies and whether they hit the ground with their heads, can effectively predict the severity of patients' injuries, which is conducive to early diagnosis, early care, and early treatment, thus reducing preventable death.
Humans ; Male ; Female ; Middle Aged ; Injury Severity Score ; Hospital Mortality ; Adult ; Accidental Falls/statistics & numerical data* ; Glasgow Coma Scale ; Intensive Care Units ; Length of Stay/statistics & numerical data* ; Wounds and Injuries/psychology* ; Abbreviated Injury Scale

This study aimed to explore key quality control factors that affected the prognosis of intensive care unit (ICU) patients in Chinese mainland over six years (2015-2020). The data for this study were from 31 provincial and municipal hospitals (3425 hospital ICUs) and included 2 110 685 ICU patients, for a total of 27 607 376 ICU hospitalization days. We found that 15 initially established quality control indicators were good predictors of patient prognosis, including percentage of ICU patients out of all inpatients (%), percentage of ICU bed occupancy of total inpatient bed occupancy (%), percentage of all ICU inpatients with an APACHE II score ⩾15 (%), three-hour (surviving sepsis campaign) SSC bundle compliance (%), six-hour SSC bundle compliance (%), rate of microbe detection before antibiotics (%), percentage of drug deep venous thrombosis (DVT) prophylaxis (%), percentage of unplanned endotracheal extubations (%), percentage of patients reintubated within 48 hours (%), unplanned transfers to the ICU (%), 48-h ICU readmission rate (%), ventilator associated pneumonia (VAP) (per 1000 ventilator days), catheter related blood stream infection (CRBSI) (per 1000 catheter days), catheter-associated urinary tract infections (CAUTI) (per 1000 catheter days), in-hospital mortality (%). When exploratory factor analysis was applied, the 15 indicators were divided into 6 core elements that varied in weight regarding quality evaluation: nosocomial infection management (21.35%), compliance with the Surviving Sepsis Campaign guidelines (17.97%), ICU resources (17.46%), airway management (15.53%), prevention of deep-vein thrombosis (14.07%), and severity of patient condition (13.61%). Based on the different weights of the core elements associated with the 15 indicators, we developed an integrated quality scoring system defined as F score=21.35%xnosocomial infection management + 17.97%xcompliance with SSC guidelines + 17.46%×ICU resources + 15.53%×airway management + 14.07%×DVT prevention + 13.61%×severity of patient condition. This evidence-based quality scoring system will help in assessing the key elements of quality management and establish a foundation for further optimization of the quality control indicator system.
Humans ; China/epidemiology* ; Cross Infection/epidemiology* ; Intensive Care Units/statistics & numerical data* ; Quality Control ; Quality Indicators, Health Care/statistics & numerical data* ; Sepsis/therapy* ; East Asian People/statistics & numerical data*

OBJECTIVE: To find the pathogenies and risk factors related to surgical intensive care unit (SICU) readmission for patients who underwent hepatobiliary-pancreatic surgery, and to develop a predictive model for determining patients who are likely to be readmitted to SICU. METHODS: The patients who admitted to SICU of the Affiliated Hospital of Qingdao University from January 2013 to August 2018; who first stayed in SICU after hepatobiliary-pancreatic surgery; who were assessed and discharged from SICU by surgeons and SICU physicians after treatment, and then transferred to SICU again because of the change of their condition were enrolled. The unintended return to SICU within 3 days and 7 days were recorded. Patients who returned to SICU within 7 days were studied for the pathogenies, risk factors and predictive model of returning to SICU, and non-returning patients were enrolled according to 1:1 as the controls. A total of 43 indicators were divided into five categories, including general clinical data, medical history, surgical indicators before first admission of SICU, length of first SICU stay, and other indicators on the day of first discharge from the SICU. Logistic regression was used to screen the risk factors associated with SICU readmission, then the Nomogram diagram was drawn by using the R 3.4.1 software for predicting SICU readmission, and the classification performance of Nomogram was evaluated by self-help sampling test. RESULTS: Of the 763 patients discharged from the SICU, 2.10% (16/763) of them were readmitted within 3 days and 3.28% (25/763) were readmitted within 7 days to the SICU unexpectedly. The pathogenies of SICU readmission within 7 days included infection [56.00% (14/25)], heart failure [16.00% (4/25)], infarction [12.00% (3/25)], bleeding [12.00% (3/25)], and sutures splitting [4.00% (1/25)]. The pathogenies of SICU readmission within 3 days included infection [56.25% (9/16)], heart failure [18.75% (3/16)], infarction [12.50% (2/16)], and bleeding [12.50% (2/16)]. Nomogram analysis showed that the risk factors associated with unplanned SICU readmission were length of first SICU stay, history of hypertension, and activity of daily living (ADL) score, white blood cell count (WBC), arterial partial pressure of oxygen (PaO₂), prothrombin time (PT), fibrinogen (FIB) on the day of first SICU discharge. Self-help sampling test was carried out on the Nomogram map, and the results showed that the coherence index (C-index) was 0.962 [95% confidence interval (95%CI) = 0.869-1.057]. The classification performance of the model was good. CONCLUSIONS The common pathogenies of SICU readmission for patients who underwent hepatobiliary-pancreatic surgery were infection, heart failure, infarction and bleeding. Risk factors of readmission after SICU discharge included the length of first SICU stay, history of hypertension, and ADL score, WBC, PaO₂, PT, FIB on the day of first SICU discharge. The model consisted of above risk factors showed a good performance in predicting the probability of readmission after SICU discharge for patients who underwent hepatobiliary-pancreatic surgery.
Biliary Tract Diseases/surgery* ; Digestive System Surgical Procedures/adverse effects* ; Humans ; Intensive Care Units ; Liver Diseases/surgery* ; Models, Statistical ; Pancreatic Diseases/surgery* ; Patient Readmission/statistics & numerical data* ; Postoperative Complications/therapy* ; Risk Factors

Adolescent ; Adult ; Cross-Sectional Studies ; Female ; Health Knowledge, Attitudes, Practice ; Humans ; Intensive Care Units ; statistics & numerical data ; Male ; Middle Aged ; Organ Transplantation ; psychology ; statistics & numerical data ; Surveys and Questionnaires ; Tissue and Organ Procurement ; statistics & numerical data ; Young Adult

PURPOSE: Fat embolism syndrome (FES) is systemic manifestation of fat emboli in the circulation seen mostly after long bone fractures. FES is considered a lethal complication of trauma. There are various case reports and series describing FES. Here we describe the clinical characteristics, management in ICU and outcome of these patients in level I trauma center in a span of 6 months. METHODS: In this prospective study, analysis of all the patients with FES admitted in our polytrauma intensive care unit (ICU) of level I trauma center over a period of 6 months (from August 2017 to January 2018) was done. Demographic data, clinical features, management in ICU and outcome were analyzed. RESULTS: We admitted 10 cases of FES. The mean age of patients was 31.2 years. The mean duration from time of injury to onset of symptoms was 56 h. All patients presented with hypoxemia and petechiae but central nervous system symptoms were present in 70% of patients. The mean duration of mechanical ventilation was 11.7 days and the mean length of ICU stay was 14.7 days. There was excellent recovery among patients with no neurological deficit. CONCLUSION FES is considered a lethal complication of trauma but timely management can result in favorable outcome. FES can occur even after fixation of the fracture. Hypoxia is the most common and earliest feature of FES followed by CNS manifestations. Any patient presenting with such symptoms should raise the suspicion of FES and mandate early ICU referral.
Adolescent ; Adult ; Central Nervous System Diseases ; etiology ; Early Diagnosis ; Embolism, Fat ; diagnosis ; etiology ; prevention & control ; Fractures, Bone ; complications ; Humans ; Hypoxia ; etiology ; Intensive Care Units ; statistics & numerical data ; Length of Stay ; statistics & numerical data ; Male ; Patient Outcome Assessment ; Time Factors ; Trauma Centers ; statistics & numerical data ; Young Adult

PURPOSE: To examine the relationships between emergency department length of stay (EDLOS) with hospital length of stay (HLOS) and clinical outcome in hemodynamically stable trauma patients. METHODS: Prospective data collected for 2 years from consecutive trauma patients admitted to the trauma resuscitation bay. Only stable blunt trauma patients with appropriate trauma triage criteria requiring trauma team activation were included in the study. EDLOS was determined short if patient spent less than 2 h in the emergency department (ER) and long for more than 2 h. RESULTS: A total of 248 patients were enrolled in the study. The mean total EDLOS was 125 min (range 78-180). Injury severity score (ISS) were significantly higher in the long EDLOS group (17 ± 13 versus 11 ± 9, p < 0.001). However, when leveled according to ISS, there were no differences in mean in diagnostic workup, admission rate to intensive care unit (ICU) or HLOS between the short and long EDLOS groups. CONCLUSION EDLOS is not a significant parameter for HLOS in stable trauma patients.
Emergency Service, Hospital ; statistics & numerical data ; Hospitals ; statistics & numerical data ; Intensive Care Units ; statistics & numerical data ; Israel ; Length of Stay ; Patient Admission ; statistics & numerical data ; Patient Outcome Assessment ; Time Factors ; Trauma Severity Indices ; Wounds and Injuries

Candida ; pathogenicity ; Cross Infection ; microbiology ; prevention & control ; Gram-Negative Bacteria ; pathogenicity ; Humans ; Intensive Care Units ; statistics & numerical data ; Sepsis ; microbiology ; prevention & control