To investigate the efficacy of amiodarone and lidocaine in cardiac arrest patients.

OBJECTIVE: To assess the relationship between blood pressure reactivity index (BPRI) and in-hospital mortality risk in patients with sepsis-associated acute kidney injury (SA-AKI). METHODS: A retrospective cohort study was conducted to collect data from patients admitted to the intensive care unit (ICU) and clinically diagnosed with SA-AKI between 2008 and 2019 in the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database in the United States. The collected data included demographic characteristics, comorbidities, vital signs, laboratory parameters, sequential organ failure assessment (SOFA) and simplified acute physiology scoreII(SAPSII) within 48 hours of SA-AKI diagnosis, stages of AKI, treatment regimens, mean BPRI during the first and second 24 hours (BPRI_0_24, BPRI_24_48), and outcome measures including primary outcome (in-hospital mortality) and secondary outcomes (ICU length of stay and total hospital length of stay). Variables with statistical significance in univariate analysis were included in LASSO regression analysis for variable selection, and the selected variables were subsequently incorporated into multivariate Logistic regression analysis to identify independent predictors associated with in-hospital mortality in SA-AKI patients. Restricted cubic spline (RCS) analysis was employed to examine whether there was a linear relationship between BPRI within 48 hours and in-hospital mortality in SA-AKI patients. Basic prediction models were constructed based on the independent predictors identified through multivariate Logistic regression analysis, and receiver operator characteristic curve (ROC curve) was plotted to evaluate the predictive performance of each basic prediction model before and after incorporating BPRI. RESULTS: A total of 3 517 SA-AKI patients admitted to the ICU were included, of whom 826 died during hospitalization and 2 691 survived. The BPRI values within 48 hours of SA-AKI diagnosis were significantly lower in the death group compared with the survival group [BPRI_0_24: 4.53 (1.81, 8.11) vs. 17.39 (5.16, 52.43); BPRI_24_48: 4.76 (2.42, 12.44) vs. 32.23 (8.85, 85.52), all P < 0.05]. LASSO regression analysis identified 20 variables with non-zero coefficients that were included in the multivariate Logistic regression analysis. The results showed that respiratory rate, temperature, pulse oxygen saturation (SpO₂), white blood cell count (WBC), hematocrit (HCT), activated partial thromboplastin time (APTT), lactate, oxygenation index, SOFA score, fluid balance (FB), BPRI_0_24, and BPRI_24_48 were all independent predictors for in-hospital mortality in SA-AKI patients (all P < 0.05). RCS analysis revealed that both BPRI showed "L"-shaped non-linear relationships with the risk of in-hospital mortality in SA-AKI patients. When BPRI_0_24 ≤ 14.47 or BPRI_24_48 ≤ 24.21, the risk of in-hospital mortality in SA-AKI increased as BPRI values decreased. Three basic prediction models were constructed based on the identified independent predictors: Model 1 (physiological indicator model) included respiratory rate, temperature, SpO₂, and oxygenation index; Model 2 (laboratory indicator model) included WBC, HCT, APTT, and lactate; Model 3 (scoring indicator model) included SOFA score and FB. ROC curve analysis showed that the predictive performance of the basic models ranked from high to low as follows: Model 3, Model 2, and Model 1, with area under the curve (AUC) values of 0.755, 0.661, and 0.655, respectively. The incorporation of BPRI indicators resulted in significant improvement in the discriminative ability of each model (all P < 0.05), with AUC values increasing to 0.832 for Model 3+BPRI, 0.805 for Model 2+BPRI, and 0.808 for Model 1+BPRI. CONCLUSIONS BPRI is an independent predictor factor for in-hospital mortality in SA-AKI patients. Incorporating BPRI into the prediction model for in-hospital mortality risk in SA-AKI can significantly improve its predictive capability.
Humans ; Acute Kidney Injury/mortality* ; Sepsis/complications* ; Retrospective Studies ; Hospital Mortality ; Prognosis ; Blood Pressure ; Intensive Care Units ; Male ; Female ; Length of Stay ; Middle Aged ; Aged ; Adult ; Logistic Models

Atypical hemolytic uremic syndrome(aHUS),a rare disease caused by complement abnor-malities,is characterized by microangiopathic hemolytic anemia,thrombocytopenia,and acute kidney injury.In this paper,we report a patient with severe renal insufficiency with rapidly progressive decline in binocular visual acuity,who developed eosinophilia during the course of the disease,and was diagnosed with aHUS after excluding other diseases.After glucocorticoid treatment,eosinophils decreased to normal,and after treatment with plasmapheresis combined with eculizumab,renal function tended to be stable,platelets returned to normal,but visual acuity did not improve significantly.This article reviews the diagnosis and treatment process of this patient and incorporates the review of literature,in the hope of providing reference for clinicians.

OBJECTIVE: To explore the genetic etiology for a Chinese pedigree affected with X-linked cardiac valve dysplasia (CVDPX) and congenital chronic pseudo intestinal obstruction (CIIPX). METHODS: A pedigree presented at the First Hospital of Lanzhou University for CVDPX combined with CIIX was selected as the study subject. Whole exome sequencing (Trio-WES) was carried out, and the candidate variant was verified by Sanger sequencing. This study has been approved by the Medical Ethics Committee of the First Hospital of Lanzhou University (Ethics No. LDYYSZLLKH2024-15). RESULTS: Both the proband and his affected younger brother were found to harbor a hemizygous c.443A>G (p.Tyr148Cys) variant of the FLNA gene, for which their mother was heterozygous and their father was not a carrier, suggesting an X-linked recessive inheritance pattern. The variant was not recorded in the OMIM and ClinVar databases, and was determined to be likely pathogenic (PM2+PS4+PP2+PP3) based on the guidelines from the American College of Medical Genetics and Genomics (ACMG). The patients had presented with typical CVDPX/CIIPX phenotype, including multiple valve dysplasia and chronic pseudo intestinal obstruction, in addition with gallbladder wall edema and thickening. Bioinformatic analysis showed that the variant site is highly conserved, and multiple algorithms had predicted its pathogenicity. CONCLUSION This study confirmed the diagnosis of CVDPX/CIIX in a Chinese pedigree, expanded the phenotype spectrum of FLNA gene variants, and provided a basis for genetic counseling and prenatal diagnosis for the pedigree.
Adult ; Female ; Humans ; Male ; Exome Sequencing ; Filamins/genetics* ; Genetic Diseases, X-Linked/genetics* ; Heart Defects, Congenital/genetics* ; Heart Valve Diseases/genetics* ; Pedigree ; East Asian People/genetics*

Objective:To explore the genetic etiology for a Chinese pedigree affected with X-linked cardiac valve dysplasia (CVDPX) and chronic idiopathic intestinal pseudo-obstruction (CIIPX).Methods:A pedigree presented at the First Hospital of Lanzhou University in June 2024 for CVDPX combined with CIIPX was selected as the study subject. Whole exome sequencing (Trio-WES) was carried out, and the candidate variant was verified by Sanger sequencing. This study has been approved by the Medical Ethics Committee of the First Hospital of Lanzhou University (Ethics No. LDYYSZLLKH2024-15).Results:Both the proband and his affected younger brother were found to harbor a hemizygous c. 443A>G (p.Tyr148Cys) variant of the FLNA gene, for which their mother was heterozygous and their father was not a carrier, suggesting an X-linked recessive inheritance pattern. The variant was not recorded by the OMIM and ClinVar databases, and was determined to be likely pathogenic (PM2+ PS4+ PP2+ PP3) based on the guidelines from the American College of Medical Genetics and Genomics (ACMG). The patients had presented with typical CVDPX/CIIPX phenotype, including multiple valve dysplasia and chronic pseudo intestinal obstruction, in addition with gallbladder wall edema and thickening. Bioinformatic analysis showed that the variant site is highly conserved, and multiple algorithms had predicted its pathogenicity. Conclusion:This study confirmed the diagnosis of CVDPX/CIIPX in a Chinese pedigree, expanded the phenotype spectrum of FLNA gene variants, and provided a basis for genetic counseling and prenatal diagnosis for the pedigree.

Objective To investigate the incidence and types of patient-ventilator asynchrony(PVA)in mechanically ventilated patients within the intensive care unit(ICU),and to identify associated high-risk factors,thereby providing a basis for reducing PVA,enhancing mechanical ventilation efficiency,and refining ventilation strategies.Methods A prospective observational study was conducted among patients admitted to the general ICU of the Affiliated Hospital of Guizhou Medical University from October to December 2024 who were receiving mechanical ventilation.Inclusion criteria were as follows:age ≥18 years and mechanical ventilation duration ≥12 hours.Exclusion criteria included complete controlled mechanical ventilation,palliative care or do-not-resuscitate status,and lack of informed consent.Senior respiratory therapists performed daily bedside observations of ventilator waveforms for 10～15 minutes between 08:00 and 12:00.PVA was diagnosed based on pressure-time and flow-time waveforms,with the types of PVA being recorded.Demographic and clinical data,including age,sex,body mass index(BMI),primary diagnosis,comorbidities,APACHEⅡ score at ICU admission,blood gas analysis,ventila-tion mode and parameters,analgesia and sedation status,duration of mechanical ventilation,and length of ICU stay,were collected.The incidence and types of PVA during the observation period were analyzed.Univariate and multivariate logistic regression analyses were performed to identify high-risk factors for PVA.Clinical outcomes were compared between patients with and without PVA.Results A total of 105 patients and 453 episodes of assisted mechanical ventilation waveforms were analyzed.Among these,60.95％(64/105)experienced at least one episode of PVA.Of the 453 ventilation waveforms assessed,35.76％(162/453)demonstrated PVA.The types of PVA,ranked by incidence,were as follows:cycling mismatch(12.58％,57/453),double triggering(11.92％,54/453),ineffective triggering(9.49％,43/453),flow starvation(5.30％,24/453),and exhalation flow limitation(1.77％,8/453).The incidence of PVA varied significantly across different ventilation modes:45.7％in volume-assist/control ventilation(V-A/C),38.1％in pressure-assist/control ventilation(P-A/C),42.9％in synchronized intermittent mandatory ventilation(SIMV),and 16.7％in pressure support ventilation(PSV)(P<0.001).Multi-variate logistic regression analysis revealed that the mechanical ventilation mode[reference:PSV;V-A/C:OR=4.687,95％CI:2.140～10.263,P<0.001;P-A/C:OR=2.922,95％CI:1.489～5.734,P=0.002;SIMV:OR=4.682,95％CI:1.758～12.466,P=0.002]and actual respiratory rate(OR=1.07,95％CI:1.016～1.127,P=0.011)were significant high-risk factors for PVA.Patients with PVA had a significantly longer duration of mechanical ventilation[8.21(5.35,13.91)days vs.3.00(1.96,5.71)days,P<0.001]compared to those without PVA.Conclusions PVA is commonly observed in ICU patients receiving assisted invasive mechanical ventilation,with cycling mismatch,double triggering,and ineffective triggering being the most prevalent types.The incidence of PVA tends to be lower when using the PSV mode.Clinically,real-time monitoring of patient-ventilator synchrony via ventilator waveforms,along with the optimization of ventilator modes and parameters,should be employed to minimize the occurrence of PVA and enhance the efficiency of mechanical ventilation.

Objective To investigate the incidence and types of patient-ventilator asynchrony(PVA)in mechanically ventilated patients within the intensive care unit(ICU),and to identify associated high-risk factors,thereby providing a basis for reducing PVA,enhancing mechanical ventilation efficiency,and refining ventilation strategies.Methods A prospective observational study was conducted among patients admitted to the general ICU of the Affiliated Hospital of Guizhou Medical University from October to December 2024 who were receiving mechanical ventilation.Inclusion criteria were as follows:age ≥18 years and mechanical ventilation duration ≥12 hours.Exclusion criteria included complete controlled mechanical ventilation,palliative care or do-not-resuscitate status,and lack of informed consent.Senior respiratory therapists performed daily bedside observations of ventilator waveforms for 10～15 minutes between 08:00 and 12:00.PVA was diagnosed based on pressure-time and flow-time waveforms,with the types of PVA being recorded.Demographic and clinical data,including age,sex,body mass index(BMI),primary diagnosis,comorbidities,APACHEⅡ score at ICU admission,blood gas analysis,ventila-tion mode and parameters,analgesia and sedation status,duration of mechanical ventilation,and length of ICU stay,were collected.The incidence and types of PVA during the observation period were analyzed.Univariate and multivariate logistic regression analyses were performed to identify high-risk factors for PVA.Clinical outcomes were compared between patients with and without PVA.Results A total of 105 patients and 453 episodes of assisted mechanical ventilation waveforms were analyzed.Among these,60.95％(64/105)experienced at least one episode of PVA.Of the 453 ventilation waveforms assessed,35.76％(162/453)demonstrated PVA.The types of PVA,ranked by incidence,were as follows:cycling mismatch(12.58％,57/453),double triggering(11.92％,54/453),ineffective triggering(9.49％,43/453),flow starvation(5.30％,24/453),and exhalation flow limitation(1.77％,8/453).The incidence of PVA varied significantly across different ventilation modes:45.7％in volume-assist/control ventilation(V-A/C),38.1％in pressure-assist/control ventilation(P-A/C),42.9％in synchronized intermittent mandatory ventilation(SIMV),and 16.7％in pressure support ventilation(PSV)(P<0.001).Multi-variate logistic regression analysis revealed that the mechanical ventilation mode[reference:PSV;V-A/C:OR=4.687,95％CI:2.140～10.263,P<0.001;P-A/C:OR=2.922,95％CI:1.489～5.734,P=0.002;SIMV:OR=4.682,95％CI:1.758～12.466,P=0.002]and actual respiratory rate(OR=1.07,95％CI:1.016～1.127,P=0.011)were significant high-risk factors for PVA.Patients with PVA had a significantly longer duration of mechanical ventilation[8.21(5.35,13.91)days vs.3.00(1.96,5.71)days,P<0.001]compared to those without PVA.Conclusions PVA is commonly observed in ICU patients receiving assisted invasive mechanical ventilation,with cycling mismatch,double triggering,and ineffective triggering being the most prevalent types.The incidence of PVA tends to be lower when using the PSV mode.Clinically,real-time monitoring of patient-ventilator synchrony via ventilator waveforms,along with the optimization of ventilator modes and parameters,should be employed to minimize the occurrence of PVA and enhance the efficiency of mechanical ventilation.

Objective:To explore the genetic etiology for a Chinese pedigree affected with X-linked cardiac valve dysplasia (CVDPX) and chronic idiopathic intestinal pseudo-obstruction (CIIPX).Methods:A pedigree presented at the First Hospital of Lanzhou University in June 2024 for CVDPX combined with CIIPX was selected as the study subject. Whole exome sequencing (Trio-WES) was carried out, and the candidate variant was verified by Sanger sequencing. This study has been approved by the Medical Ethics Committee of the First Hospital of Lanzhou University (Ethics No. LDYYSZLLKH2024-15).Results:Both the proband and his affected younger brother were found to harbor a hemizygous c. 443A>G (p.Tyr148Cys) variant of the FLNA gene, for which their mother was heterozygous and their father was not a carrier, suggesting an X-linked recessive inheritance pattern. The variant was not recorded by the OMIM and ClinVar databases, and was determined to be likely pathogenic (PM2+ PS4+ PP2+ PP3) based on the guidelines from the American College of Medical Genetics and Genomics (ACMG). The patients had presented with typical CVDPX/CIIPX phenotype, including multiple valve dysplasia and chronic pseudo intestinal obstruction, in addition with gallbladder wall edema and thickening. Bioinformatic analysis showed that the variant site is highly conserved, and multiple algorithms had predicted its pathogenicity. Conclusion:This study confirmed the diagnosis of CVDPX/CIIPX in a Chinese pedigree, expanded the phenotype spectrum of FLNA gene variants, and provided a basis for genetic counseling and prenatal diagnosis for the pedigree.

Atypical hemolytic uremic syndrome (aHUS), a rare disease caused by complement abnormalities, is characterized by microangiopathic hemolytic anemia, thrombocytopenia, and acute kidney injury. In this paper, we report a patient with severe renal insufficiency with rapidly progressive decline in binocular visual acuity, who developed eosinophilia during the course of the disease, and was diagnosed with aHUS after excluding other diseases. After glucocorticoid treatment, eosinophils decreased to normal, and after treatment with plasmapheresis combined with eculizumab, renal function tended to be stable, platelets returned to normal, but visual acuity did not improve significantly. This article reviews the diagnosis and treatment process of this patient and incorporates the review of literature, in the hope of providing reference for clinicians.

BACKGROUND:Sepsis is one of the main causes of mortality in intensive care units(ICUs).Early prediction is critical for reducing injury.As approximately 36%of sepsis occur within 24 h after emergency department(ED)admission in Medical Information Mart for Intensive Care(MIMIC-IV),a prediction system for the ED triage stage would be helpful.Previous methods such as the quick Sequential Organ Failure Assessment(qSOFA)are more suitable for screening than for prediction in the ED,and we aimed to find a light-weight,convenient prediction method through machine learning. METHODS:We accessed the MIMIC-IV for sepsis patient data in the EDs.Our dataset comprised demographic information,vital signs,and synthetic features.Extreme Gradient Boosting(XGBoost)was used to predict the risk of developing sepsis within 24 h after ED admission.Additionally,SHapley Additive exPlanations(SHAP)was employed to provide a comprehensive interpretation of the model's results.Ten percent of the patients were randomly selected as the testing set,while the remaining patients were used for training with 10-fold cross-validation. RESULTS:For 10-fold cross-validation on 14,957 samples,we reached an accuracy of 84.1%±0.3%and an area under the receiver operating characteristic(ROC)curve of 0.92±0.02.The model achieved similar performance on the testing set of 1,662 patients.SHAP values showed that the five most important features were acuity,arrival transportation,age,shock index,and respiratory rate. CONCLUSION:Machine learning models such as XGBoost may be used for sepsis prediction using only a small amount of data conveniently collected in the ED triage stage.This may help reduce workload in the ED and warn medical workers against the risk of sepsis in advance.