Background

The Hypotension Prediction Index (the index) software is a machine learning algorithm that detects physiological changes that may lead to hypotension. The original validation used a case control (backwards) analysis that has been suggested to be biased. We therefore conducted a cohort (forwards) analysis and compared this to the original validation technique.

Methods

We conducted a retrospective analysis of data from previously reported studies. All data were analysed identically with 2 different methodologies and receiver operating characteristic curves (ROC) constructed. Both backwards and forwards analyses were performed to examine differences in area under the ROC for HPI and other haemodynamic variables to predict a MAP < 65mmHg for at least 1 minute 5, 10 and 15 minutes in advance.

Results

Two thousand and twenty-two patients were included in the analysis, yielding 4,152,124 measurements taken at 20 second intervals. The area-under-the-curve for the index predicting hypotension analysed by backward and forward methodologies respectively was 0.957 (95% CI, 0.947-0.964) vs 0.923 (95% CI, 0.912-0.933) 5 minutes in advance, 0.933 (95% CI, 0.924-0.942) vs 0.923 (95% CI, 0.911-0.933) 10 minutes in advance , and 0.929 (95% CI, 0.918-0.938) vs. 0.926 (95% CI, 0.914-0.937) 15 minutes in advance. No other variable had an area-under-the-curve > 0.7 except for MAP. Area-under-the-curve using forward analysis for MAP predicting hypotension 5, 10, and 15 minutes in advance was 0.932 (95% CI, 0.920-0.940), 0.929 (95% CI, 0.918-0.938), and 0.932 (95% CI, 0.921-0.940). The R 2 for the variation in the index due to MAP was 0.77.

Conclusion

Using an updated methodology, we found the utility of the HPI index to predict future hypotensive events is high, with an area under the receiver-operating-characteristics curve similar to that of the original validation method.

Background

Transfusion-related acute lung injury (TRALI) is a serious complication of blood transfusion and is among the leading causes of transfusion-related morbidity and mortality in most developed countries. In the past decade, the pathophysiology of this potentially life-threatening syndrome has been increasingly elucidated, large cohort studies have identified associated patient conditions and transfusion risk factors, and preventive strategies have been successfully implemented. These new insights provide a rationale for updating the 2004 consensus definition of TRALI.

Study design and methods

An international expert panel used the Delphi methodology to develop a redefinition of TRALI by modifying and updating the 2004 definition. Additionally, the panel reviewed issues related to TRALI nomenclature, patient conditions associated with acute respiratory distress syndrome (ARDS) and TRALI, TRALI pathophysiology, and standardization of reporting of TRALI cases.

Results

In the redefinition, the term "possible TRALI" has been dropped. The terminology of TRALI Type I (without an ARDS risk factor) and TRALI Type II (with an ARDS risk factor or with mild existing ARDS) is proposed. Cases with an ARDS risk factor that meet ARDS diagnostic criteria and where respiratory deterioration over the 12 hours before transfusion implicates the risk factor as causative should be classified as ARDS. TRALI remains a clinical diagnosis and does not require detection of cognate white blood cell antibodies.

Conclusions

Clinicians should report all cases of posttransfusion pulmonary edema to the transfusion service so that further investigation can allow for classification of such cases as TRALI (Type I or Type II), ARDS, transfusion-associated circulatory overload (TACO), or TRALI or TACO cannot distinguish or an alternate diagnosis.

Background and aims

Thrombosis is a major driver of adverse outcome and mortality in patients with Coronavirus disease 2019 (COVID-19). Hypercoagulability may be related to the cytokine storm associated with COVID-19, which is mainly driven by interleukin (IL)-6. Plasma lipoprotein(a) [Lp(a)] levels increase following IL-6 upregulation and Lp(a) has anti-fibrinolytic properties. This study investigated whether Lp(a) elevation may contribute to the pro-thrombotic state hallmarking COVID-19 patients.

Methods

Lp(a), IL-6 and C-reactive protein (CRP) levels were measured in 219 hospitalized patients with COVID-19 and analyzed with linear mixed effects model. The baseline biomarkers and increases during admission were related to venous thromboembolism (VTE) incidence and clinical outcomes in a Kaplan-Meier and logistic regression analysis.

Results

Lp(a) levels increased significantly by a mean of 16.9 mg/dl in patients with COVID-19 during the first 21 days after admission. Serial Lp(a) measurements were available in 146 patients. In the top tertile of Lp(a) increase, 56.2% of COVID-19 patients experienced a VTE event compared to 18.4% in the lowest tertile (RR 3.06, 95% CI 1.61-5.81; p < 0.001). This association remained significant after adjusting for age, sex, IL-6 and CRP increase and number of measurements. Increases in IL-6 and CRP were not associated with VTE. Increase in Lp(a) was strongly correlated with increase in IL-6 (r = 0.44, 95% CI 0.30-0.56, p < 0.001).

Conclusions

Increases in Lp(a) levels during the acute phase of COVID-19 were strongly associated with VTE incidence. The acute increase in anti-fibrinolytic Lp(a) may tilt the balance to VTE in patients hospitalized for COVID-19.