Predicting survival in patients with acute decompensated heart failure complicated by cardiogenic shock

COMPARISON OF THE PREDICTIVE PERFORMANCE OF CARDIOGENIC SHOCK SCORES IN A REAL-WORLD LATIN AMERICA COUNTRY

ABSTRACT

Background : Mortality in cardiogenic shock (CS) is up to 40%, and although risk scores have been proposed to stratify and assess mortality in CS, they have been shown to have inconsistent performance. The purpose was to compare CS prognostic scores and describe their performance in a real-world Latin American country. Methods : We included 872 patients with CS. The Society for Cardiovascular Angiography and Interventions (SCAI), CARDSHOCK, IABP-Shock II, Cardiogenic Shock Score, age-lactate-creatinine score, Get-With-The-Guidelines Heart Failure score, and Acute Decompensated Heart Failure National Registry scores were calculated. Decision curve analyses were performed to evaluate the net benefit of the different scoring systems. Logistic and Cox regression analyses were applied to construct area under the curve (AUC) statistics, this last one against time using the Inverse Probability of Censoring Weighting method, for in-hospital mortality prediction. Results: When logistic regression was applied, the scores had a moderate-good performance in the overall cohort that was higher AUC in the CARDSHOCK ( c = 0.666). In acute myocardial infarction-related CS (AMI-CS), CARDSHOCK still is the highest AUC (0.68). In non-AMI-CS only SCAI (0.668), CARDSHOCK (0.533), and IABP-SHOCK II (0.636) had statistically significant values. When analyzed over time, significant differences arose in the AUC, suggesting that a time-sensitive component influenced the prediction of mortality. The highest AUC was for the CARDSHOCK score (0.658), followed by SCAI (0.622). In AMI-CS-related, the highest AUC was for the CARDSHOCK score (0.671). In non-AMI-CS, SCAI was the best (0.642). Conclusions : Clinical scores show a time-sensitive AUC, suggesting that performance could be influenced by time and the type of CS. Understanding the temporal influence on the scores could provide a better prediction and be a valuable tool in CS.

Nomogram for predicting the prognosis of tumor patients with sepsis after gastrointestinal surgery

BACKGROUND

There were few studies on the prognosis of tumor patients with sepsis after gastrointestinal surgery and there was no relevant nomogram for predicting the prognosis of these patients.

AIM

To establish a nomogram for predicting the prognosis of tumor patients with sepsis after gastrointestinal surgery in the intensive care unit (ICU).

METHODS

A total of 303 septic patients after gastrointestinal tumor surgery admitted to the ICU at Peking University Cancer Hospital from January 1, 2013 to December 31, 2020 were analysed retrospectively. The model for predicting the prognosis of septic patients was established by the R software package.

RESULTS

The most common infection site of sepsis after gastrointestinal surgery in the ICU was abdominal infection. The 90-d all-cause mortality rate was 10.2% in our study group. In multiple analyses, we found that there were statistically significant differences in tumor type, septic shock, the number of lymphocytes after ICU admission, serum creatinine and total operation times among tumor patients with sepsis after gastrointestinal surgery (P < 0.05). These five variables could be used to establish a nomogram for predicting the prognosis of these septic patients. The nomogram was verified, and the initial C-index was 0.861. After 1000 internal validations of the model, the C-index was 0.876, and the discrimination was good. The correction curve indicated that the actual value was in good agreement with the predicted value.

CONCLUSION

The nomogram based on these five factors (tumor type, septic shock, number of lymphocytes, serum creatinine, and total operation times) could accurately predict the prognosis of tumor patients with sepsis after gastrointestinal surgery.

A Survival Prediction for Acute Heart Failure Patients via Web-Based Dynamic Nomogram with Internal Validation: A Prospective Cohort Study

Purpose

The current study aimed to develop a convenient and accurate prognostic dynamic nomogram model for the risk of all-cause death in acute heart failure (AHF) patients that incorporates clinical characteristics including N-terminal pro-brain natriuretic peptide (NT-pro BNP) and growth stimulation expresses gene 2 protein (ST2).

Patients and Methods

We prospectively studied 537 consecutive AHF patients and derived a clinical prediction model. The least absolute shrinkage and selection operator regression model combined with clinical characteristics were used for dimensional reduction and feature selection. Multivariate Cox proportional hazard analysis and “Dynnom” package were used to build the dynamic nomogram for prediction of 1-,2-,and 5-year overall survival for AHF. With bootstrap validation, the time-dependent concordance index (C-index) and calibration curves were used to assess predictive discrimination and accuracy. The contributions of NT-pro BNP and ST2 to the nomogram were evaluated using integrated discrimination improvement (IDI) and net reclassification improvement (NRI), while decision curve analysis (DCA) was used to assess clinical value.

Results

Patients were randomly divided into derivation (74.9%, n=402) and validation (25.1%, n=135) cohorts. Optimal independent prognostic factors for 1-,2-, and 5-year all-cause mortality were BS-ACMR (B: NT-pro BNP; S: ST2; A: age; C: complete right bundle branch block; M: mean arterial pressure; and R: red cell distribution width >14.5%); these were incorporated into the dynamic nomogram (https://bs-acmr-nom.shinyapps.io/dynnomapp/) with bootstrap validation. The C-indexes in the derivation (0.793) and validation (0.782) cohorts were consistent with comparable performance parameters. The calibration curve showed good agreement between the nomogram-predicted and actual survival. Adding NT-pro BNP and ST2 provided a significant net benefit and improved performance over other less adequate schemes in terms of DCA of survival probability compared to those neglecting either of these two factors.

Conclusion

The study constructed a dynamic BS-ACMR nomogram, which is a convenient, practical and effective clinical decision-making tool for providing accurate prognosis in AHF patients.