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Clinical validation of a computerized algorithm to determine mean systemic filling pressure.

ABSTRACT: Mean systemic filling pressure (Pms) is a promising parameter in determining intravascular fluid status. Pms derived from venous return curves during inspiratory holds with incremental airway pressures (Pms-Insp) estimates Pms reliably but is labor-intensive. A computerized algorithm to calculate Pms (Pmsa) at the bedside has been proposed. In previous studies Pmsa and Pms-Insp correlated well but with considerable bias. This observational study was performed to validate Pmsa with Pms-Insp in cardiac surgery patients. Cardiac output, right atrial pressure and mean arterial pressure were prospectively recorded to calculate Pmsa using a bedside monitor. Pms-Insp was calculated offline after performing inspiratory holds. Intraclass-correlation coefficient (ICC) and assessment of agreement were used to compare Pmsa with Pms-Insp. Bias, coefficient of variance (COV), precision and limits of agreement (LOA) were calculated. Proportional bias was assessed with linear regression. A high degree of inter-method reliability was found between Pmsa and Pms-Insp (ICC 0.89; 95%CI 0.72-0.96, p = 0.01) in 18 patients. Pmsa and Pms-Insp differed not significantly (11.9 mmHg, IQR 9.8-13.4 vs. 12.7 mmHg, IQR 10.5-14.4, p = 0.38). Bias was -0.502 ± 1.90 mmHg (p = 0.277). COV was 4% with LOA -4.22 - 3.22 mmHg without proportional bias. Conversion coefficient Pmsa ➔ Pms-Insp was 0.94. This assessment of agreement demonstrates that the measures Pms-Insp and the computerized Pmsa-algorithm are interchangeable (bias -0.502 ± 1.90 mmHg with conversion coefficient 0.94). The choice of Pmsa is straightforward, it is non-interventional and available continuously at the bedside in contrast to Pms-Insp which is interventional and calculated off-line. Further studies should be performed to determine the place of Pmsa in the circulatory management of critically ill patients. ( www.clinicaltrials.gov ; TRN NCT04202432, release date 16-12-2019; retrospectively registered).Clinical Trial Registration www.ClinicalTrials.gov , TRN: NCT04202432, initial release date 16-12-2019 (retrospectively registered).

SUBMITTER: Meijs LPB

PROVIDER: S-EPMC8011774 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background: A decade ago, it became possible to derive mean systemic filling pressure (MSFP) at the bedside using the inspiratory hold maneuver. MSFP has the potential to help guide hemodynamic care, but the estimation is not yet implemented in common clinical practice. In this study, we assessed the ability of MSFP, vascular compliance (Csys), and stressed volume (Vs) to track fluid boluses. Second, we assessed the feasibility of implementation of MSFP in the intensive care unit (ICU). Exploratory, a potential difference in MSFP response between colloids and crystalloids was assessed. Methods: This was a prospective cohort study in adult patients admitted to the ICU after cardiac surgery. The MSFP was determined using 3-4 inspiratory holds with incremental pressures (maximum 35 cm H2O) to construct a venous return curve. Two fluid boluses were administered: 100 and 500 ml, enabling to calculate Vs and Csys. Patients were randomized to crystalloid or colloid fluid administration. Trained ICU consultants acted as study supervisors, and protocol deviations were recorded. Results: A total of 20 patients completed the trial. MSFP was able to track the 500 ml bolus (p < 0.001). In 16 patients (80%), Vs and Csys could be determined. Vs had a median of 2029 ml (IQR 1605-3164), and Csys had a median of 73 ml mmHg-1 (IQR 56-133). A difference in response between crystalloids and colloids was present for the 100 ml fluid bolus (p = 0.019) and in a post hoc analysis, also for the 500 ml bolus (p = 0.010). Conclusion: MSFP can be measured at the bedside and provides insights into the hemodynamic status of a patient that are currently missing. The clinical feasibility of Vs and Csys was judged ambiguously based on the lack of required hemodynamic stability. Future studies should address the clinical obstacles found in this study, and less-invasive alternatives to determine MSFP should be further explored. Clinical Trial Registration: ClinicalTrials.gov Identifier NCT03139929.

Project description:ObjectivesElevated intracranial pressure (ICP) is a potentially devastating complication of neurologic injury. Developing an ICP prediction algorithm to help the clinician adjust treatments and potentially prevent elevated ICP episodes.DesignRetrospective study.SettingThree hundred thirty-five ICUs at 208 hospitals in the United States.SubjectsAdults patients from the electronic ICU (eICU) Collaborative Research Database was used to train an ensemble machine learning model to predict the ICP 30 minutes in the future. Predictive performance was evaluated using a left-out test dataset and externally evaluated on the Medical Information Mart for Intensive Care-III (MIMIC-III) Matched Waveform Database.InterventionsNone.Measurements and main resultsPredictors included age, assigned sex, laboratories, medications and infusions, input/output, Glasgow Coma Scale (GCS) components, and time-series vitals (heart rate, ICP, mean arterial pressure, respiratory rate, and temperature). Each patient ICU stay was divided into successive 95-minute timeblocks. For each timeblock, the model was trained on nontime-varying covariates as well as on 12 observations of time-varying covariates at 5-minute intervals and asked to predict the 5-minute median ICP 30 minutes after the last observed ICP value. Data from 931 patients with ICP monitoring in the eICU dataset were extracted (46,207 timeblocks). The root mean squared error was 4.51 mm Hg in the eICU test set and 3.56 mm Hg in the MIMIC-III dataset. The most important variables driving ICP prediction were previous ICP history, patients' temperature, weight, serum creatinine, age, GCS, and hemodynamic parameters.ConclusionsIntraCranial pressure prediction AlgoRithm using machinE learning, an ensemble machine learning model, trained to predict the ICP of a patient 30 minutes in the future based on baseline characteristics and vitals data from the past hour showed promising predictive performance including in an external validation dataset.

Project description:Biofilms are the preferred sessile and matrix-embedded life form of most microorganisms on surfaces. In the medical field, biofilms are a frequent cause of treatment failure because they protect the bacteria from antibiotics and immune cells. Antibiotics are selected according to the minimal inhibitory concentration (MIC) based on the planktonic form of bacteria. Determination of the minimal biofilm eradicating concentration (MBEC), which can be up to 1,000-fold greater than the MIC, is not currently conducted as routine diagnostic testing, primarily because of the methodical hurdles of available biofilm assessing protocols that are time- and cost-consuming. Comparative analysis of biofilms is also limited as most quantitative methods such as crystal violet staining are indirect and highly imprecise. In this paper, we present a novel algorithm for assessing biofilm resistance to antibiotics that overcomes several of the limitations of alternative methods. This algorithm aims for a computer-based analysis of confocal microscope 3D images of biofilms after live/dead stains providing various biofilm parameters such as numbers of viable and dead cells and their vertical distributions within the biofilm, or biofilm thickness. The performance of this algorithm was evaluated using computer-simulated 2D and 3D images of coccal and rodent cells varying different parameters such as cell density, shading or cell size. Finally, genuine biofilms that were untreated or treated with nitroxoline or colistin were analyzed and the results were compared with quantitative microbiological standard methods. This novel algorithm allows a direct, fast and reproducible analysis of biofilms after live/dead staining. It performed well in biofilms of moderate cell densities in a 2D set-up however the 3D analysis remains still imperfect and difficult to evaluate. Nevertheless, this is a first try to develop an easy but conclusive tool that eventually might be implemented into routine diagnostics to determine the MBEC and to improve outcomes of patients with biofilm-associated infections.

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Clinical validation of a computerized algorithm to determine mean systemic filling pressure.

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