Blood lactate predicts survival after percutaneous implantation of extracorporeal life support for refractory cardiac arrest or cardiogenic shock complicating acute coronary syndrome: insights from the CareGem registry

Standby extracorporeal membrane oxygenation: a better strategy for high-risk percutaneous coronary intervention

Background

The incidence of cardiac arrest (CA) during percutaneous coronary intervention (PCI) is relatively rare. However, when it does occur, the mortality rate is extremely high. Extracorporeal cardiopulmonary resuscitation (ECPR) has shown promising survival rates for in-hospital cardiac arrests (IHCA), with low-flow time being an independent prognostic factor for CA. However, there is no definitive answer on how to reduce low-flow time.

Methods

This retrospective study, conducted at a single center, included 39 patients who underwent ECPR during PCI between January 2016 and December 2022. The patients were divided into two cohorts based on whether standby extracorporeal membrane oxygenation (ECMO) was utilized during PCI: standby ECPR (SBE) (n = 13) and extemporaneous ECPR (EE) (n = 26). We compared the 30-day mortality rates between these two cohorts and investigated factors associated with survival.

Results

Compared to the EE cohort, the SBE cohort showed significantly lower low-flow time (P < 0.01), ECMO operation time (P < 0.01), and a lower incidence of acute kidney injury (AKI) (P = 0.017), as well as peak lactate (P < 0.01). Stand-by ECMO was associated with improved 30-day survival (p = 0.036), while prolonged low-flow time (p = 0.004) and a higher SYNTAX II score (p = 0.062) predicted death at 30 days.

Conclusions

Standby ECMO can provide significant benefits for patients who undergo ECPR for CA during PCI. It is a viable option for high-risk PCI cases and may enhance the overall prognosis. The low-flow time remains a critical determinant of survival.

A simple APACHE IV risk dynamic nomogram that incorporates early admitted lactate for the initial assessment of 28-day mortality in critically ill patients with acute myocardial infarction

BACKGROUND

Early risk stratification is important for patients with acute myocardial infarction (AMI). We aimed to develop a simple APACHE IV dynamic nomogram, combined with easily available clinical parameters within 24 h of admission, thus improving its predictive power to assess the risk of mortality at 28 days.

METHODS

Clinical information on AMI patients was extracted from the eICU database v2.0. A preliminary XGBoost examination of the degree of association between all variables in the database and 28-day mortality was conducted. Univariate and multivariate logistic regression analysis were used to perform screening of variables. Based on the multifactorial analysis, a dynamic nomogram predicting 28-day mortality in these patients was developed. To cope with missing data in records with missing variables, we applied the multiple imputation method. Predictive models are evaluated in three main areas, namely discrimination, calibration, and clinical validity. The discrimination is mainly represented by the area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Calibration is represented by the calibration plot. Clinical validity is represented by the decision curve analysis (DCA) curve.

RESULTS

A total of 504 people were included in the study. All 504 people were used to build the predictive model, and the internal validation model used a 500-bootstrap method. Multivariate analysis showed that four variables, APACHE IV, the first sample of admission lactate, prior atrial fibrillation (AF), and gender, were included in the nomogram as independent predictors of 28-day mortality in AMI. The prediction model had an AUC of 0.819 (95%CI 0.770-0.868) whereas the internal validation model had an AUC of 0.814 (95%CI 0.765-0.860). Calibration and DCA curves indicated that the dynamic nomogram in this study were reflective of real-world conditions and could be applied clinically. The predictive model composed of these four variables outperformed a single APACHE IV in terms of NRI and IDI. The NRI was 16.4% (95% CI: 6.1-26.8%; p = 0.0019) and the IDI was 16.4% (95% CI: 6.0-26.8%; p = 0.0020). Lactate accounted for nearly half of the total NRI, which showed that lactate was the most important of the other three variables.

CONCLUSION

The prediction model constructed by APACHE IV in combination with the first sample of admission lactate, prior AF, and gender outperformed the APACHE IV scoring system alone in predicting 28-day mortality in AMI. The prediction dynamic nomogram model was published via a website app, allowing clinicians to improve the predictive efficacy of the APACHE IV score by 16.4% in less than 1 min.

Radiographic assessment of lung edema (RALE) score is associated with clinical outcomes in patients with refractory cardiogenic shock and refractory cardiac arrest after percutaneous implantation of extracorporeal life support

VA-ECMO is a promising therapeutic option in refractory cardiogenic shock (RCS) and refractory cardiac arrest (RCA). However, increase in left ventricular afterload enhances further reduction of LV contractility and pulmonary edema. The aim of this study was to evaluate pulmonary edema based on the RALE score and the prognostic value of the score on ECLS weaning and mortality. In this retrospective study, data from 40 patients (16 RCAs and 24 RCSs) were analyzed. Demographic, clinical data and the RALE score for evaluating pulmonary edema were assessed. Descriptive statistics, intraclass correlation, and receiver operating characteristic (ROC) curves were computed. Weaning from ECLS was successful in 30 (75%) patients, 16 patients (40%) were discharged alive. Overall, the survivors were younger, presenting with a higher left ventricular ejection fraction (30 ± 2% vs.23 ± 9%;p < 0.01) and a lower initial serum lactate concentration 7.7 ± 4.5 mmol/l vs. 11.5 ± 4.9 mmol/l; p = 0.017). Survivors had lower RALE scores than non-survivors (16.3 ± 9.4 vs. 26.4 ± 10.4; p = 0.0034). The interobserver variability of the RALE score was good (0.832). The AUC predicting mortality and weaning from ECLS presented comparable results to the established parameters (SAVE, serum lactate). Implementation of the RALE score could support prediction of outcome parameters during VA-ECMO therapy.

Establishing prognostic significance of hypoxia predictors in patients with acute cerebral pathology

OBJECTIVES

This research aims to study the prognostic role of serum S100 as a predictor of mortality in vascular and traumatic brain injuries.

METHODS

This prospective cohort study involved 219 patients. In the blood serum, neuron-specific markers (S100, NSE) and glucose, acid-base state and gas composition of arterial blood were obtained at admission, on the 3rd, 5th and 7th days of patients' stay in the intensive care unit.

RESULTS

The most significant risk factor for an unfavorable outcome is the marker S100 with a cut-off point of 0.2 mcg/l. The analysis results indicate a statistically significant direct relationship between S100 > 0.2 mcg/l and NSE ≥ 18.9 ng/ml compared to other variables, while the chance ratio (OR) is 11.9 (95%CI:3.2927-1.6693;). With blood sugar increase above 7.4 mmol/l, the OR is 3.82 (95% CI: 2.1289-0.5539;); with a Glasgow scale below 13 points, the OR is 3.69 (95% CI: 2.1316-0.4819;); with an increase in pCO2 < 43.5 mm Hg, the OR was 3.15 (95% CI: 1.8916- 0.4062;). The obtained model certainty measure according to pseudo R2 Nagelkerke criterion is 263.5, showing the excellent quality of the mathematical model's predictive ability. The developed prognostic model, including the dependent variable S100 and independent variables as predictors of a poor outcome of NSE, pCO2, GCS and Hb, reached a cut-off point of 84.51%, AUC - 0.88 with high levels of sensitivity and specificity: 91.89% and 64.14%, respectively.

NOVELTY

This model can be used to predict the outcome in patients with acute cerebral pathology.

Initial Blood pH, Lactate and Base Deficit Add No Value to Peri-Arrest Factors in Prognostication of Neurological Outcome After Out-of-Hospital Cardiac Arrest

Background: In cardiac arrest survivors, metabolic parameters [pH value, lactate concentration, and base deficit (BD)] are routinely added to peri-arrest factors (including age, sex, bystander cardiopulmonary resuscitation, shockable first rhythm, resuscitation duration, adrenaline dose) to enhance early outcome prediction. However, the additional value of this strategy remains unclear.

Methods: We used our resuscitation database to screen all patients ≥18 years who had suffered in- or out-of-hospital cardiac arrest (IHCA, OHCA) between January 1st, 2005 and May 1st, 2019. Patients with incomplete data, without return of spontaneous circulation or treatment with sodium bicarbonate were excluded. To analyse the added value of metabolic parameters to prognosticate neurological function, we built three models using logistic regression. These models included: (1) Peri-arrest factors only, (2) peri-arrest factors plus metabolic parameters and (3) metabolic parameters only. Receiver operating characteristics curves regarding 30-day good neurological function (Cerebral Performance Category 1-2) were analysed.

Results: A total of 2,317 patients (OHCA: n = 1842) were included. In patients with OHCA, model 1 and 2 had comparable predictive value. Model 3 was inferior compared to model 1. In IHCA patients, model 2 performed best, whereas both metabolic (model 3) and peri-arrest factors (model 1) demonstrated similar power. PH, lactate and BD had interchangeable areas under the curve in both IHCA and OHCA.

Conclusion: Although metabolic parameters may play a role in IHCA, no additional value in the prediction of good neurological outcome could be found in patients with OHCA. This highlights the importance of accurate anamnesis especially in patients with OHCA.

A prognostic nomogram for long-term major adverse cardiovascular events in patients with acute coronary syndrome after percutaneous coronary intervention

BACKGROUND

Accurate prediction of major adverse cardiovascular events (MACEs) is very important for the management of acute coronary syndrome (ACS) patients. We aimed to construct an effective prognostic nomogram for individualized risk estimates of MACEs for patients with ACS after percutaneous coronary intervention (PCI).

METHODS

This was a prospective study of patients with ACS after PCI from January 2013 to July 2019 (n = 2465). After removing patients with incomplete clinical information, a total of 1986 patients were randomly divided into evaluation (n = 1324) and validation (n = 662) groups. Predictors included in the nomogram were determined by a multivariate Cox proportional hazards regression model based on the training set. Receiver operating characteristic (ROC) curves and calibration curves were used to assess the discrimination and predictive accuracy of the nomogram, which were then compared with those of the classic models. The clinical utility of the nomogram was assessed by X-tile analysis and Kaplan-Meier curve analysis.

RESULTS

Independent prognostic factors, including lactate level, age, left anterior descending branch stenosis, right coronary artery stenosis, brain natriuretic peptide level, and left ventricular ejection fraction, were determined and contained in the nomogram. The nomogram achieved good areas under the ROC curve of 0.712-0.762 in the training set and 0.724-0.818 in the validation set and well-fitted calibration curves. In addition, participants could be divided into two risk groups (low and high) according to this model.

CONCLUSIONS

A simple-to-use nomogram incorporating lactate level effectively predicted 6-month, 1-year, and 4-year MACE incidence among patients with ACS after PCI.