Chest pain management: use of troponins in internal medicine wards

Establishment of a nomogram model for acute chest pain triage in the chest pain center

Background

Acute myocardial infarction (AMI) is the leading life-threatening disease in the emergency department (ED), so rapid chest pain triage is important. This study aimed to establish a clinical prediction model for the risk stratification of acute chest pain patients based on the Point-of-care (POC) cardiac troponin (cTn) level and other clinical variables.

Methods

We conducted a post-hoc analysis of the database from 6,019 consecutive patients (excluding prehospital-diagnosed non-cardiac chest pain patients) attending a local chest pain center (CPC) in China between October 2016 and January 2019. The plasma concentration of cardiac troponin I (cTnI) was measured using a POC cTnI (Cardio Triage, Alere) assay. All the eligible patients were randomly divided into training and validation cohorts by a 7:3 ratio. We performed multivariable logistic regression to select variables and build a nomogram based on the significant predictive factors. We evaluated the model's generalization ability of diagnostic accuracy in the validation cohort.

Results

We analyzed data from 5,397 patients that were included in this research. The median turnaround time (TAT) of POC cTnI was 16 min. The model was constructed with 6 variables: ECG ischemia, POC cTnI level, hypotension, chest pain symptom, Killip class, and sex. The area under the ROC curve (AUC) in the training and validation cohorts was 0.924 and 0.894, respectively. The diagnostic performance was superior to the GRACE score (AUC: 0.737).

Conclusion

A practical predictive model was created and could be used for rapid and effective triage of acute chest pain patients in the CPC.

Psychometric properties of the STAI-Y scales and normative data in an Italian elderly population

BACKGROUND

Early detection of anxiety symptoms in older people is capital as it may be linked to increased physical/functional disabilities, onset and progression of neurodegenerative disorders, and poor cognitive functioning. Nonetheless, there is a paucity of psychometrically validated anxiety measures in the elderly.

AIMS

This study aimed at assessing the psychometric properties of the State-Trait Anxiety Inventory-Form Y (STAI-Y) and providing the first normative data for the Italian elderly population.

METHODS

The sample included 361 individuals aged 65-94. All subjects underwent the Mini-Mental State Examination (MMSE), the Geriatric Depression Scale (GDS), and the state (S-Anxiety) and trait anxiety (T-anxiety) scales of the STAI-Y.

RESULTS

The S-Anxiety and T-Anxiety scales showed reliable internal consistency and, overall, good item characteristics. Divergent validity was "apparently" threatened, with S-Anxiety scale correlating with MMSE and GDS, and T-Anxiety scale only with GDS. The principal component analysis revealed a three-factor solution for both scales, i.e., presence and absence of state (or trait) anxiety, and performance anxiety. Since no effect of sociodemographic variables was found, unadjusted cutoffs were provided.

CONCLUSIONS

Although some questions on the psychometric properties of the STAI-Y remain unanswered, this normative study can help clinicians and researchers to monitor anxiety levels in the Italian elderly population.