OBJECTIVES

Clinical pathways (CPs) ensure adherence to heart failure (HF) management guidelines. To optimize quality care in a low resource setting, an evidence-based care pathway for the management of acute HF was implemented at the emergency department (ED) of the Philippine General Hospital (PGH), the designated national tertiary hospital and referral center. This study aimed to describe the characteristics of adults with acute HF admitted at the ED and evaluate the quality of care they received, measured using physician adherence to the hospital’s acute heart failure CP.

METHODS

This was a retrospective, descriptive cohort study. We reviewed the inpatient charts of all adult patients with acute HF admitted to the ED of the PGH and referred to the Division of Cardiovascular Medicine between December 1, 2022 and May 31, 2023. Quality of care was assessed based on adherence to quality indicators adapted from routine and conditional order sets detailed in the pathway. Descriptive statistics was utilized to describe patient characteristics, quality of care, and outcomes.

RESULTS

Two hundred thirty-six (236) patients were included, with a mean age of 51.8 years. Majority were male (53.4%); hypertension (61.4%) and ischemic heart disease (53.8%) were the most common comorbidities, and infection the most common precipitant of decompensation (60.6%). There were optimal adherence rates to routine orders, which included referrals to Internal Medicine and Cardiology, baseline vital signs monitoring, fluid intake and output monitoring, chest radiograph, complete blood count, blood urea nitrogen, sodium, potassium, prothrombin time, partial thromboplastin time, arterial blood gas, urinalysis, and N-terminal pro b-type natriuretic peptide. Conditional orders, such as oxygen support, focused echocardiography, thyroid - stimulating hormone, and the use of vasopressors, diuretics, and venous thromboembolism prophylactic agents, were optimally performed when warranted. However, we noted suboptimal adherence to certain resource-intensive conditional orders, such as hourly monitoring of urine output (61.4%), hooking to cardiac monitor (53.8%), and performance of 12-lead ECG within 10 minutes (56.8%). Further, only 43.9% of patients were referred to the intensive care unit. Troponin I, calcium, magnesium, and albumin were ordered in excess.

CONCLUSION

Overall adherence rate of physicians to the hospital’s Acute Heart Failure Pathway was satisfactory. Work is needed to improve adherence to hourly urine output monitoring, consistent hooking to cardiac monitor, and timely performance of 12-lead ECG – an effort that begins with expanding in-hospital diagnostic equipment and human resource supply. We recommend continuous pathway implementation with periodic evaluation and stakeholder feedback to further improve quality of care.

Human ; Male ; Female ; Middle Aged: 45-64 Yrs Old ; Adult ; Albumins ; Blood ; Blood Urea Nitrogen ; Calcium ; Cardiology ; Chart ; Charts ; Cohort Studies ; Critical Care ; Critical Pathways ; Diagnostic Equipment ; Disease ; Diuretics ; Echocardiography ; Electrocardiography ; Emergencies ; Emergency Service, Hospital ; Equipment And Supplies ; Evaluation Studies As Topic ; Feedback ; Heart ; Heart Diseases ; Heart Failure ; Hormones ; Hospitals ; Hospitals, General ; Humans ; Hypertension ; Indicators And Reagents ; Infection ; Infections ; Inpatients ; Intensive Care Units ; Internal Medicine ; Lead ; Magnesium ; Male ; Medicine ; Myocardial Ischemia ; Natriuretic Peptide, Brain ; Natriuretic Peptides ; Nitrogen ; Overall ; Oxygen ; Partial Thromboplastin Time ; Patients ; Peptides ; Philippines ; Physicians ; Potassium ; Prothrombin ; Prothrombin Time ; Quality Of Health Care ; Referral And Consultation ; Sodium ; Statistics ; Tertiary Care Centers ; Thorax ; Thromboembolism ; Thromboplastin ; Thyroid Gland ; Time ; Troponin ; Troponin I ; Universities ; Urea ; Urinalysis ; Urine ; Venous Thromboembolism ; Vital Signs ; Work ; Workforce

Acute Care Surgery (ACS) is an emerging discipline of general surgery that integrates trauma, emergency surgery, critical care medicine, and surgical rescue. It is a modern model for the diagnosis and treatment of patients with acute and critical abdominal conditions. Compared to the traditional model, ACS integrates the theories and techniques of trauma, critical care, and surgery. It consolidates surgical wards and intensive care units into a single department for operational management. The care of acute care patients is led by physicians who are qualified in both critical care medicine and surgery. This model improves efficiency and significantly reduces morbidity and mortality of patients. Both international and domestic hospitals have had multiple surgical rescue teams that have embarked on exploratory work in the development of ACS, accumulating a certain amount of experience. The Expert Working Group of Acute Care Surgery, Chinese Medical Doctor Association Division of Surgeons has formed a preliminary guideline for the development of the Department of Acute Care Surgery based on the current experience and accomplishment in China for the reference of hospitals at all levels.
Critical Care ; Humans ; General Surgery ; Acute Care Surgery

The accurate and timely detection of biochemical coagulation indicators is pivotal in pulmonary and critical care medicine. Despite their reliability, traditional laboratories often lag in terms of rapid diagnosis. Point-of-care testing (POCT) has emerged as a promising alternative, which is awaiting rigorous validation. We assessed 226 samples from patients at the First Affiliated Hospital of Guangzhou Medical University using a Beckman Coulter AU5821 and a PUSHKANG POCT Biochemistry Analyzer MS100. Furthermore, 350 samples were evaluated with a Stago coagulation analyzer STAR MAX and a PUSHKANG POCT Coagulation Analyzer MC100. Metrics included thirteen biochemical indexes, such as albumin, and five coagulation indices, such as prothrombin time. Comparisons were drawn against the PUSHKANG POCT analyzer. Bland-Altman plots (MS100: 0.8206‒0.9995; MC100: 0.8318‒0.9911) evinced significant consistency between methodologies. Spearman correlation pinpointed a potent linear association between conventional devices and the PUSHKANG POCT analyzer, further underscored by a robust correlation coefficient (MS100: 0.713‒0.949; MC100: 0.593‒0.950). The PUSHKANG POCT was validated as a dependable tool for serum and whole blood biochemical and coagulation diagnostics. This emphasizes its prospective clinical efficacy, offering clinicians a swift diagnostic tool and heralding a new era of enhanced patient care outcomes.
Humans ; Point-of-Care Testing ; Critical Care ; Blood Coagulation Tests/methods* ; Male ; Blood Coagulation ; Female ; Middle Aged ; Reproducibility of Results ; Prothrombin Time ; Aged ; Adult ; Point-of-Care Systems

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 systematically summarize and analyze the project applications and funding in the field of Emergency and Critical Care Medicine by the Medical Science Department of the National Natural Science Foundation of China (NSFC) from 2010 to 2024, and to identify research hotspots and developmental trends, providing scientific references for the high-quality development of the Emergency and Critical Care Medicine in China. METHODS: Data on all project applications and funding in the field of Emergency and Critical Care Medicine (application code H16) from 2010 to 2024 were collected from the NSFC Grants System, including project application numbers, funding numbers and amounts, project categories, regional and affiliated institutions distributions. Keyword co-occurrence analysis was conducted using VOSviewer software to identify research hotspots, and results were presented using bar charts, pie charts, and Sankey diagrams. RESULTS: Over the past 15 years, the Emergency and Critical Care Medicine field of NSFC received 13 747 project applications and funded 1 781 projects, with a cumulative funding amount of 8.064 99 billion RMB. The annual number of applications increased from 296 in 2010 to 1 971 in 2024, representing an average annual growth rate of 40.42%. Similarly, the number of funded projects grew from 45 in 2010 to 175 in 2024, with an average annual growth rate of 20.63%, while annual funding rose from 20.01 million RMB in 2010 to 74.20 million RMB in 2024, reflecting an average annual growth rate of 19.34%. The majority of funded projects belonged to the General Program (774 projects), Young Scientists Fund (754 projects), and Regional Science Fund (163 projects), collectively accounting for 94.95% of total funded projects (1 691/1 781). Funding was concentrated in two primary research areas: Organ Dysfunction and Support (H1602, 751 projects) and Sepsis (H1601, 612 projects), together comprising 76.53% of total funded projects (1 363/1 781). The total number of funded projects (1 781 projects) in Emergency and Critical Care Medicine was fewer than the average across the subfields of Medical Science Department (4 181 projects). Shanghai (305 projects, 17.1%), Guangdong (222 projects, 12.5%), Jiangsu (154 projects, 8.6%), Zhejiang (149 projects, 8.4%), and Beijing (134 projects, 7.5%) ranked as the top five regions in terms of funded projects. Keyword co-occurrence analysis revealed that sepsis, organ injury, pulmonary injury and poisoning, and cardiopulmonary resuscitation were the main research hotspots in the field of Emergency and Critical Care Medicine over the past 15 years. CONCLUSION From 2010 to 2024, the NSFC funding for the field of Emergency and Critical Care Medicine has shown a significant upward trajectory, providing vital support for the rapid advancement of basic and applied research. This growth has played a crucial role in facilitating the high-quality development of Emergency and Critical Care Medicine in China.
China ; Critical Care/economics* ; Emergency Medicine/economics* ; Humans ; Foundations

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

Intra-abdominal candidiasis (IAC) is the most common invasive candidiasis, with a high incidence among critically ill patients, which can significantly increase medical costs and affect prognosis. In order to standardize the diagnosis and treatment of IAC in critically ill patients, experts in related fields were organized by the Peking University Critical Care Medicine (PKUCCM), Committee of Critical Care Medicine and Organ Support, China Association for Promotion of Health Science and Technology organized experts in related fields to initiate and form a working group. Expert writers drafted the consensus based on evidence-based medical evidence. A committee composed of critical care physicians, infectious disease physicians, surgeons, dermatologists specializing in antifungal fields, and clinical pharmacists discussed and revised the consensus draft through a standardized process, and finally formulated this consensus. This consensus contains a total of 20 core recommendations, mainly focusing on the epidemiology, high-risk factors, diagnostic techniques and methods (including traditional microbiological culture techniques, clinical risk prediction tools, serological tests, molecular biological tests, and histopathological examinations) of IAC, diagnostic criteria, stratified treatment strategies, antifungal drug selection, control the sources of infection, combined treatment, de-escalation strategies, drug treatment courses, prognosis, and special types of IAC. The aim is to provide expert guidance for the standardized clinical diagnosis and treatment of IAC in critically ill patients, with a view to improving prognosis of patients.
Humans ; Critical Illness ; Intraabdominal Infections/therapy* ; Antifungal Agents/therapeutic use* ; Consensus ; Candidiasis/drug therapy* ; Critical Care ; Candidiasis, Invasive/diagnosis*

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