Prediction of hemodynamic tolerance of intermittent hemodialysis in critically ill patients: a cohort study.
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SUBMITTER: da Hora Passos R
PROVIDER: S-EPMC8655072 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundDespite improvements in intermittent hemodialysis management, intradialytic hemodynamic instability (IHI) remains a common issue that could account for increased mortality and delayed renal recovery. However, predictive factors of IHI remain poorly explored. The objective of this study was to evaluate the relationship between baseline macrohemodynamic, tissue hypoperfusion parameters and IHI occurrence.MethodsProspective observational study conducted in a 18-bed medical ICU of a tertiary teaching hospital. Cardiovascular SOFA score, index capillary refill time (CRT) and lactate level were measured just before (T0) consecutive intermittent hemodialysis sessions performed for AKI. The occurrence of IHI requiring a therapeutic intervention was recorded.ResultsTwo hundred eleven sessions, corresponding to 72 (34%) first sessions and 139 (66%) later sessions, were included. As IHI mostly occurred during first sessions (43% vs 12%, P < 0.0001), following analyses were performed on the 72 first sessions. At T0, cardiovascular SOFA score ≥1 (87% vs 51%, P = 0.0021) was more frequent before IHI sessions, as well as index CRT ≥ 3 s (55% vs 15%, P = 0.0004), and hyperlactatemia > 2 mmol/L (68% vs 29%, P = 0.0018). Moreover, the occurrence of IHI increased with the number of macrohemodynamic and tissue perfusion impaired parameters, named SOCRATE score (cardiovascular SOFA, index CRT and lactATE): 10% (95% CI [3%, 30%]), 33% (95% CI [15%, 58%]), 55% (95% CI [35%, 73%]) and 80% (95% CI [55%, 93%]) for 0, 1, 2 and 3 parameters, respectively (AUC = 0.79 [0.69-0.89], P < 0.0001). These results were confirmed by analyzing the 139 later sessions included in the study.ConclusionsThe SOCRATE score based on 3 easy-to-use bedside parameters correlates with the risk of IHI. By improving risk stratification of IHI, this score could help clinicians to manage intermittent hemodialysis initiation in critically ill AKI patients.
| S-EPMC7176798 | biostudies-literature
Project description:IntroductionDexmedetomidine has been suggested to be a promising sedative for patients with Covid-19 infection (CV19). However, use of dexmedetomidine is limited by its heart rate (HR) and arterial blood pressure lowering effects. Moreover, CV19 is associated with cardiac manifestations including bradyarrythmias. The hemodynamic effects of dexmedetomidine have not been previously studied in CV19 patients. We evaluated the effects of dexmedetomidine on hemodynamic and respiratory parameters of CV19 patients.MethodsIn this single center study, all CV19 patients receiving dexmedetomidine for sedation during a one year period were included. Our primary outcomes included changes in HR, mean arterial pressure (MAP), respiratory rate (RR), partial oxygen pressure of arterial blood/fraction of inspired oxygen-ratio (PF-ratio), and Richmond Agitation and Sedation Score (RASS) during dexmedetomidine administration.ResultsWe identified 39 patients with a mean (SD) age of 58.3 (12.7) years. After initiation of dexmedetomidine, HR decreased by 16.9 (3.3) beats/min (95% CI 9.5-22.4; p < 0.001). During the 12-hour follow-up period, HR decrease was significant at 2 to 12 h. Incident bradycardia (<45/min) was reported in 12 (30.8%) patients and it was associated with lower plasma C-reactive protein, Pro-calcitonin, and troponin T levels. There was no change in MAP compared to baseline. Dexmedetomidine administration was associated with improvement of PF-ratio (p < 0.001) and with decrease of RASS (p = 0.004).ConclusionsDexmedetomidine is an effective sedative for CV19 patients and may improve their oxygenation. However, dexmedetomidine administration is associated with marked decline in HR and with a high incidence of bradycardia in patients with CV19.
| S-EPMC8441884 | biostudies-literature
Project description:Colistin is increasingly used as a last option for the treatment of severe infections due to Gram-negative bacteria in critically ill patients requiring intermittent hemodialysis (HD) for acute renal failure. Our objective was to characterize the pharmacokinetics (PK) of colistin and its prodrug colistin methanesulfonate (CMS) in this population and to suggest dosing regimen recommendations. Eight intensive care unit (ICU) patients who were under intermittent HD and who were treated by CMS (Colimycine) were included. Blood samples were collected between two consecutive HD sessions. CMS and colistin concentrations were measured by a specific chromatographic assay and were analyzed using a PK population approach (Monolix software). Monte Carlo simulations were conducted to predict the probability of target attainment (PTA). CMS nonrenal clearance was increased in ICU-HD patients. Compared with that of ICU patients included in the same clinical trial but with preserved renal function, colistin exposure was increased by 3-fold in ICU-HD patients. This is probably because a greater fraction of the CMS converted into colistin. To maintain colistin plasma concentrations high enough (>3 mg/liter) for high PTA values (area under the concentration-time curve for the free, unbound fraction of a drug [fAUC]/MIC of >10 and fAUC/MIC of >50 for systemic and lung infections, respectively), at least for MICs lower than 1.5 mg/liter (nonpulmonary infection) or 0.5 mg/liter (pulmonary infection), the dosing regimen of CMS should be 1.5 million international units (MIU) twice daily on non-HD days. HD should be conducted at the end of a dosing interval, and a supplemental dose of 1.5 MIU should be administered after the HD session (i.e., total of 4.5 MIU for HD days). This study has confirmed and complemented previously published data and suggests an a priori clear and easy to follow dosing strategy for CMS in ICU-HD patients.
| S-EPMC4775947 | biostudies-literature
Project description:The PREVAIL study was a Phase II/III randomized controlled trial examining the use of lactoferrin to prevent nosocomial infections in critically ill patients undergoing mechanical ventilation. Gene expression data was generated from a consecutive subset of patients at the lead study site. We used the Affymetrix PrimeView array to generate expression data at various time points during the ICU stay.
2018-08-22 | GSE118657 | GEO
Project description:The surprise question, "Would I be surprised if this patient died in the next 12 months?" is a tool to identify patients at high risk of death in the next year. Especially in the situation of an ICU admission, it is important to recognize patients who could and could not have the benefits of an intensive treatment in the ICU department.Design and settingA single-center, prospective, observational cohort study was conducted between April 2013 and April 2018, in ICU Gelre hospitals, location Apeldoorn.PatientsA total of 3,140 patients were included (57% male) with a mean age of 63.5 years. Seven-hundred thirteen patients (23%) died within 1 year.InterventionsThe physician answered three different surprise question's with either "yes" or "no": "I expect that the patient is going to survive the ICU admission" (surprise question 1), "I expect that the patient is going to survive the hospital stay" (surprise question 2), and "I expect that the patient is going to survive one year after ICU admission" (surprise question 3). We tested positive and negative predicted values of the surprise questions, the mean accuracy of the surprise questions, and kappa statistics.Measurements and main resultsThe positive and negative predictive values of the surprise questions for ICU admission, hospital admission, and 1-year survival were, respectively, 64%/94%, 59%/92%, and 60%/86%. Accordingly, the mean accuracy and kappa statistics were 93% (95% CI, 92-94%), κ equals to 0.43, 89% (95% CI, 88-90%), κ equals to 0.40, and 81% (95% CI, 80-82%), κ equals to 0.43.ConclusionsThe frequently overlooked simple and cheap surprise question is probably an useful tool to evaluate the prognosis of acutely admitted critically ill patients.
| S-EPMC7803926 | biostudies-literature
Project description: Not available
| S-EPMC7333139 | biostudies-literature
Project description:IntroductionOnline hemodiafiltration (HDF) has been increasingly used for improved clearance of middle molecular weight toxins. The impact of this mode of clearance is unknown in critically ill patients. We aimed to determine whether the use of HDF in acute kidney injury (AKI) is associated with lower mortality and improved kidney recovery up to 90 days after initiation of therapy.MethodsSingle-center retrospective cohort study using data from 2017 to 2020 of adults with AKI who initiated intermittent renal replacement therapy (IRRT) in the intensive care unit (ICU), using either hemodialysis (HD) or HDF depending on the maintenance status of the water system without regards for patient characteristics. We assessed association with patient-events and session-events using time-dependent Cox models and general estimating equations models, respectively.ResultsWe included 182 adults with AKI for whom 848 IRRT sessions were performed in the ICU. The 90-day mortality rate was 43 of 182 (24.6%). There was no significant association with the use of HDF and mortality (adjusted hazard ratio [aHR]: 0.85 (0.43; 1.67) P = 0.64), kidney recovery (aHR: 1.18 (0.76; 1.84) P = 0.47), or intradialytic hypotension (adjusted odds ratio [aOR]: 0.91 confidence interval [CI]: 0.64-1.28 P = 0.58). HDF treatment was associated with a lower rate of subsequent vasopressor use (aOR: 0.60 CI: 0.36-0.99 P = 0.047) and a greater reduction of the neutrophil-to-lymphocyte ratio (NLR) following the first session (-15.0% vs. +5.1%, P = 0.047) but was also associated with increased risk of filter thrombosis during treatment (aOR: 2.42 CI: 1.67-3.50 P < 0.001).ConclusionThe use of HDF in the setting of AKI was not associated with a differential risk of mortality or kidney recovery.
| S-EPMC9751768 | biostudies-literature
Project description:BACKGROUND:Intradialytic blood pressure (BP) measurement is currently the main parameter used for monitoring hemodynamics during hemodialysis (HD). Since BP is dependent on cardiac output and total peripheral resistance, knowledge of these parameters throughout the HD treatment would potentially be valuable. METHODS:The use of a novel non-invasive monitoring system for profiling hemodynamic response patterns during HD was explored: a whole-body bio-impedance system was used to assess cardiac index (CI), total peripheral resistance index (TPRI), cardiac power index (CPI) among other parameters in chronic HD patients from 4 medical centers. Measurements were made pre, during and post dialysis. Patients were grouped into 5 hemodynamic profiles based on their main hemodynamic response during dialysis i.e. high TPRI; high CPI; low CPI; low TPRI and those with normal hemodynamics. Comparisons were made between the groups for baseline characteristics and 1-year mortality. RESULTS:In 144 patients with mean age of 67.3?±?12.1?years pre-dialysis hemodynamic measurements were within normal limits in 35.4% but only 6.9% overall remained hemodynamically stable during dialysis. Intradialytic BP decreased in 65 (45.1%) in whom, low CPI (47 (72.3%)) and low TPRI (18 (27.7%) were recorded. At 1-year follow-up, mortality rates were highest in patients with low CPI (23.4%) and low TPRI (22.2%). CONCLUSIONS:Non-invasive assessment of patients' response to HD provides relevant hemodynamic information that exceeds that provided by currently used BP measurements. Use of these online analyses could potentially improve the safety and performance standards of dialysis by guiding appropriate interventions, particularly in responding to hypertension and hypotension.
| S-EPMC6724365 | biostudies-literature
Project description:The effect of statin therapy on mortality in critically ill patients is controversial, with some studies suggesting a benefit and others suggesting no benefit or even potential harm. The objective of this study was to evaluate the association between statin therapy during intensive care unit (ICU) admission and all-cause mortality in critically ill patients.This was a nested cohort study within two randomised controlled trials conducted in a tertiary care ICU. All 763 patients who participated in the two trials were included in this study. Of these, 107 patients (14%) received statins during their ICU stay. The primary endpoint was all-cause ICU and hospital mortality. Secondary endpoints included the development of sepsis and severe sepsis during the ICU stay, the ICU length of stay, the hospital length of stay, and the duration of mechanical ventilation. Multivariate logistic regression was used to adjust for clinically and statistically relevant variables.Statin therapy was associated with a reduction in hospital mortality (adjusted odds ratio [aOR] = 0.60, 95% confidence interval [CI] 0.36-0.99). Statin therapy was associated with lower hospital mortality in the following groups: patients >58 years of age (aOR = 0.58, 95% CI 0.35-0.97), those with an acute physiology and chronic health evaluation (APACHE II) score >22 (aOR = 0.54, 95% CI 0.31-0.96), diabetic patients (aOR = 0.52, 95% CI 0.30-0.90), patients on vasopressor therapy (aOR = 0.53, 95% CI 0.29-0.97), those admitted with severe sepsis (aOR = 0.22, 95% CI 0.07-0.66), patients with creatinine ? 100 ?mol/L (aOR = 0.14, 95% CI 0.04-0.51), and patients with GCS ? 9 (aOR = 0.34, 95% CI 0.17-0.71). When stratified by statin dose, the mortality reduction was mainly observed with statin equipotent doses ? 40 mg of simvastatin (aOR = 0.53, 95% CI 0.28-1.00). Mortality reduction was observed with simvastatin (aOR = 0.37, 95% CI 0.17-0.81) but not with atorvastatin (aOR = 0.80, 95% CI 0.84-1.46). Statin therapy was not associated with a difference in any of the secondary outcomes.Statin therapy during ICU stay was associated with a reduction in all-cause hospital mortality. This association was especially noted in high-risk subgroups. This potential benefit needs to be validated in a randomised, controlled trial.
| S-EPMC3199769 | biostudies-other
Project description:Sepsis is defined by life-threatening organ dysfunction during infection and is the leading cause of death in hospitals. During sepsis, there is a high risk that new onset of atrial fibrillation (AF) can occur, which is associated with significant morbidity and mortality. Consequently, early prediction of AF during sepsis would allow testing of interventions in the intensive care unit (ICU) to prevent AF and its severe complications. In this paper, we present a novel automated AF prediction algorithm for critically ill sepsis patients using electrocardiogram (ECG) signals. From the heart rate signal collected from 5-min ECG, feature extraction is performed using the traditional time, frequency, and nonlinear domain methods. Moreover, variable frequency complex demodulation and tunable Q-factor wavelet-transform-based time-frequency methods are applied to extract novel features from the heart rate signal. Using a selected feature subset, several machine learning classifiers, including support vector machine (SVM) and random forest (RF), were trained using only the 2001 Computers in Cardiology data set. For testing the proposed method, 50 critically ill ICU subjects from the Medical Information Mart for Intensive Care (MIMIC) III database were used in this study. Using distinct and independent testing data from MIMIC III, the SVM achieved 80% sensitivity, 100% specificity, 90% accuracy, 100% positive predictive value, and 83.33% negative predictive value for predicting AF immediately prior to the onset of AF, while the RF achieved 88% AF prediction accuracy. When we analyzed how much in advance we can predict AF events in critically ill sepsis patients, the algorithm achieved 80% accuracy for predicting AF events 10 min early. Our algorithm outperformed a state-of-the-art method for predicting AF in ICU patients, further demonstrating the efficacy of our proposed method. The annotations of patients' AF transition information will be made publicly available for other investigators. Our algorithm to predict AF onset is applicable for any ECG modality including patch electrodes and wearables, including Holter, loop recorder, and implantable devices.
| S-EPMC8391773 | biostudies-literature