Risk determination after an acute myocardial infarction: review of 3 clinical risk prediction tools

Accuracy of death risk prediction models for acute coronary syndrome patients: a systematic review and meta-analysis

INTRODUCTION

This study systematically evaluates the accuracy of several death risk prediction models for patients with acute coronary syndrome (ACS) through evidence-based methods. We identify the most accurate and effective ACS death risk prediction model and provide an evidence-based basis for clinical healthcare personnel to evaluate their choice of death risk prediction model for ACS patients.

EVIDENCE ACQUISITION

An evidence-based approach was used to study the current death risk prediction model for ACS. First, a literature search was carried out using computer-based and manual searching. The literature databases searched include Cochrane Library, MEDLINE, EMBASE, PubMed, Web of Science, WanFang Data, CNKI, VPCS, and SinoMed. The search period was limited to 2009 to 2022. Screening, quality evaluation and data extraction were carried out for the included articles. The PROBAST was used to conduct a migration risk assessment. RevMan 5.3 and Meta-DiSc 1.4 were used in combination to determine the model effect sizes. A descriptive analysis was conducted for the data that could not be meta-analyzed.

EVIDENCE SYNTHESIS

A total of 8277 articles were initially included in this study. After screening, 25 articles were finally included, involving 11 different risk prediction models. A total of 306,390 patients with ACS were included of which 158,080 (51.6%) were male and 147,793 (48.4%) were female. The patients stemmed from 11 different countries (e.g., China, the USA, Spain, the UK, etc.). The total number of deaths was 23,601. The sensitivity of the GRACE risk prediction model was 0.78, with a specificity of 0.76 and an AUC value of 0.86. The sensitivity of the CAMI risk prediction model was 0.78, with a specificity of 0.70 and an AUC value of 0.85. The sensitivity of the TIMI risk prediction model was 0.51, with a specificity of 0.81, and an AUC value of 0.64. The sensitivity of the REMS risk prediction model was 0.78, with a specificity of 0.46 and an AUC value of 0.41. Eight different risk prediction models (EPICOR, CRUSADE, SAMI, GWTG, LNS, SYNTAX II, APACHE II) that could not be combined with the effect size were also included, with sensitivities ranging from 0.77-0.95, specificities ranging from 0.22-0.99, and AUC values ranging from 0.71-0.92.

CONCLUSIONS

The GRACE and CAMI risk prediction models demonstrate good accuracy for evaluating the death risk of ACS patients. The accuracy of the TIMI risk prediction model is similar to that of the REMS risk prediction model. The APACHE II, SYNTAX II, EPICOR, and CAMI risk prediction models also show good accuracy for estimating the risk of death in ACS patients, although further validation is needed due to limited evidence. For improved predictive accuracy and to help advance medical interventions, the author recommends that clinical medical staff use the GRACE model to predict the death risk of ACS patients.

Construção e validação das competências profissionais do enfermeiro atuante em hemodinâmica

Objetivo: construir e validar as competências profissionais para enfermeiros atuantes em Unidades de Hemodinâmica. Método: trata-se de uma pesquisa metodológica de construção e validação das competências do enfermeiro de hemodinâmica, aprovada pelo Comitê de Ética em Pesquisa sob protocolo nº 67891517.8.0000.5462 e realizada em instituição pública de cardiologia do estado de São Paulo. Na primeira etapa foi realizada a construção do instrumento para identificação das competências do enfermeiro no setor de hemodinâmica a partir de revisão de literatura e observação local e na segunda, a validação do instrumento por especialistas. Os dados foram analisados pelo Índice de Validade de Conteúdo (IVC) e média ponderada do IVC. Resultados: a validação das competências ocorreu após duas rodadas de avaliação junto aos especialistas, resultando um total de sete competências relativas ao processo de trabalho da enfermagem, com média ponderada de IVC entre 88,4 e 99,2 e 74 habilidades, as quais foram classificadas quanto ao tipo de competência, sendo 14 itens avaliados como básicos, 10 como intermediários, 34 como avançados e 16 inconclusivos. Conclusão: as competências profissionais para enfermeiros atuantes em Unidades de Hemodinâmica foram construídas e validadas por especialistas, que poderão subsidiar novas diretrizes acerca da formação e educação permanente dos profissionais nessa área.

Early risk stratification of acute myocardial infarction using a simple physiological prognostic scoring system: insights from the REACP study

BACKGROUND

A more accurate and simpler scoring systems for early risk stratification of acute myocardial infarction at admission can accelerate and improve decision-making.

AIM

To develop and validate a simple physiological prognostic scoring system for early risk stratification in patients with acute myocardial infarction.

METHODS

Easily accessible physiological vital signs and demographic characteristics of patients with acute myocardial infarction at the time of presentation in the multicentre Retrospective Evaluation of Acute Chest Pain study were used to develop a multivariate logistic regression model predicting 12 and 24-month mortality. The study population consisted of 2619 patients from seven hospitals and was divided into a 70% sample for model derivation and a 30% sample for model validation. A nomogram was created to enable prospective risk stratification for clinical care.

RESULTS

The simple physiological prognostic scoring system consisted of age, heart rate, body mass index and Killip class. The area under the receiver operating characteristic curve of the simple physiological prognostic scoring system was superior to that of several risk scoring systems in clinical use. Net reclassification improvement, integrated discrimination improvement and decision curve analysis of the derivation set also revealed superior performance to the Global Registry of Acute Coronary Events score, and the Hosmer-Lemeshow test indicated good calibration for predicting mortality in patient with acute myocardial infarction in the validation set (P = 0.612).

CONCLUSION

This simple physiological prognostic scoring system may be a useful risk stratification tool for early assessment of patients with acute myocardial infarction.

Real-Time Automated Sampling of Electronic Medical Records Predicts Hospital Mortality

BACKGROUND

Real-time automated continuous sampling of electronic medical record data may expeditiously identify patients at risk for death and enable prompt life-saving interventions. We hypothesized that a real-time electronic medical record-based alert could identify hospitalized patients at risk for mortality.

METHODS

An automated alert was developed and implemented to continuously sample electronic medical record data and trigger when at least 2 of 4 systemic inflammatory response syndrome criteria plus at least one of 14 acute organ dysfunction parameters was detected. The systemic inflammatory response syndrome and organ dysfunction alert was applied in real time to 312,214 patients in 24 hospitals and analyzed in 2 phases: training and validation datasets.

RESULTS

In the training phase, 29,317 (18.8%) triggered the alert and 5.2% of such patients died, whereas only 0.2% without the alert died (unadjusted odds ratio 30.1; 95% confidence interval, 26.1-34.5; P < .0001). In the validation phase, the sensitivity, specificity, area under the curve, and positive and negative likelihood ratios for predicting mortality were 0.86, 0.82, 0.84, 4.9, and 0.16, respectively. Multivariate Cox-proportional hazard regression model revealed greater hospital mortality when the alert was triggered (adjusted hazards ratio 4.0; 95% confidence interval, 3.3-4.9; P < .0001). Triggering the alert was associated with additional hospitalization days (+3.0 days) and ventilator days (+1.6 days; P < .0001).

CONCLUSION

An automated alert system that continuously samples electronic medical record data can be implemented, has excellent test characteristics, and can assist in the real-time identification of hospitalized patients at risk for death.

Evaluation of Stress Intensity and Anxiety Level in Preoperative Period of Cardiac Patients

Introduction. The stress related to patient's stay in a hospital increases when it is necessary to perform a surgery. Therefore, the study of the phenomenon of stress intensity in hospitalized patients has become an important issue for public health. Material and Method. The study was conducted in University Hospital No. 1 in the cardiosurgery clinic. The study involved 58 patients who were admitted as planned to the hospital. The study used a standardized questionnaire measuring intensity of the stress and also deepened interviews with patients about stress and anxiety felt before the surgery. Results. The greater the patient's anxiety resulting from his state of health, the greater the intensity of stress in the preoperative period. This relationship is linear. The results of the study also made it possible to see intrapersonal factors (pain, illness, and suffering) and extrapersonal factors (anesthesia, surgery, and complications after surgery), which are causes of anxiety before surgery. Conclusion. The research showed high (negative) results of anxiety and stress associated with the disease, surgery, and complications after cardiac surgery. Active involvement in hospitalization elements, such as patient education before surgery, psychological support, and medical care organization taking into account patient's preferences, reduces the impact of stressors.