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

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 systematically evaluate the effect of different durations of prone ventilation on the efficacy of patients with acute respiratory distress syndrome (ARDS).Methods:A computer search was conducted in databases including PubMed, Cochrane Library, Embase, CNKI, Wanfang Database, VIP Database, and China Biomedical Literature Database for studies on prone ventilation for the treatment of adult patients with ARDS published from the establishment of the database to September 2023. Studies were categorized into ≤24 hours group and > 24 hours group based on the duration of prone ventilation. Outcome indicators included mortality, the length of intensive care unit (ICU) stay, incidence of pressure ulcers, and operation of tracheotomy. Two researchers independently screened the literature, extracted information, and evaluated the risk of bias of the included literature. The quality of the included literature was assessed using the NOS scale, and the effect of different durations of prone ventilation on the efficacy of ARDS was analyzed by Meta-analysis.Results:A total of 517 patients from 4 papers were finally included, including 249 patients with prone ventilation duration ≤24 hours and 268 patients with prone ventilation duration > 24 hours. All 4 studies were cohort studies, and the overall inclusion of literature assessed for methodological quality indicated high study quality and low risk of bias. Meta-analysis showed that there were no significantly differences in mortality [relative risk ( RR) = 1.02, 95% confidence interval (95% CI) was 0.79 to 1.31, P = 0.88], the length of ICU stay [mean difference ( MD) = -2.68, 95% CI was -5.30 to - 0.05, P = 0.05] between the prone ventilation duration ≤ 24 hours group and prone ventilation duration > 24 hours group. Compared with the prone ventilation duration ≤24 hours group, the incidence of pressure ulcers ( RR = 0.76, 95% CI was 0.59 to 0.98, P = 0.04) and the operation of tracheotomy ( RR = 0.71, 95% CI was 0.53 to 0.94, P = 0.02) were significantly increased in the prone ventilation duration > 24 hours group. Conclusions:The duration of prone ventilation had no significant effect on the mortality and the length of ICU stay in ARDS patients, but prone ventilation for > 24 hours increased the incidence of pressure ulcers and the operation of tracheotomy, which still needs to be further verified by a large number of studies due to the small number of included studies.