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

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

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

Human ; Intensive Care Units ; Critical Care ; Philippines

Objective: This study described clinicodemographic profiles of children with prolonged length of stay admitted at the regular service ward of Philippine Children’s Medical Center (January 2023 – December 2023).

Materials and Methods: A descriptive and retrospective clinical profiling of patients with prolonged length of stay at PCMC was done (January 2023 to December 2023). Included were patients aged 1-18 years old admitted at the regular service ward and discussed during the overstaying audit. Excluded were those admitted at Neonatal Intensive Care Unit and Newborn Service Ward. Patients with prolonged length of stay admitted under the Hematology and Oncology Service ward and those with incomplete chart information were also excluded. Data collected were age, sex, area of residence, presence of comorbidities, diagnosis, reason for prolonged length of stay, and clinical outcomes.

Results: 153 patients were included in this study. Majority of the patients with prolonged length of stay were adolescents (43.79%). Most of the patients identified in this study were female (78%). Those who overstayed were predominantly from Quezon City (27.45%). Comorbidities were present in 93.46% of patients. Neurologic conditions accounted for majority of the admissions (35.29%). Most common reason for overstaying of patients was due to a medical reason (91.5%). Furthermore, 93.46% of patients were discharged while 6.54% died.

Conclusion: This retrospective study presented the clinical profile of patients with prolonged length of stay who were mostly adolescents, with female predominance. Neurologic disease was the most common diagnosis identified among patients. Those patients who have prolonged length of stay were generally because of medical problems mostly due to the complexity and chronicity of their disease. Strengthening of ongoing service delivery network and prompt subspecialty referrals and involvement may be recommended to address discharge delays and maximize hospital resources.

Human ; Male ; Female ; Infant: 1-23 Months ; Child Preschool: 2-5 Yrs Old ; Child: 6-12 Yrs Old ; Adolescent: 13-18 Yrs Old ; Length Of Stay ; Critical Care ; Chart

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