Gender-specific Performance of a Diagnostic Score in Acute Appendicitis

Accuracy of Prediction Models in Diagnosis of Acute Diverticulitis

BACKGROUND/AIM

The diagnostic score models (DMs) for patients with acute diverticulitis (AcDi) have been rarely evaluated. Therefore, we tried to develop diagnostic models (DMs) to enhance the diagnostic accuracy (DA) of AcDi.

PATIENTS AND METHODS

In this AAP (acute abdominal pain) cohort, 30 AcDi patients were compared to 1,303 non-AcDi patients, with regard to their i) clinical symptoms (n=22), ii) signs and tests (n=14) as well as iii) laboratory analyses (n=3). The triage was performed at patient arrival to the emergency department (ED) (triage I) and at follow-up (triage II) before final decision. The triage included a suggested diagnosis of the AAP patient. Bivariate random effects meta-analysis was performed separately for 1) the pooled symptoms (n=22), 2) signs & tests (n=17) as well as 3) pooled DMs (I-V) with different cut-offs (with or without triage) to assess the diagnostic accuracy (DA) in detection of AcDi by HSROC (hierarchical summary receiver operating characteristic) curves.

RESULTS

In the conventional receiver operating characteristic (ROC) analysis (for test optimization and finding optimal cut-off points), the area under curve (AUC) reached the following values for AcDi: i) DM without triage, AUC=0.843, ii) DM with triage I, AUC=0.866 and iii) DM with triage I and II, AUC=0.926. In the HSROC analysis, the AUC values for detection of AcDi were as follows; i) pooled clinical symptoms, AUC=0.540, ii) pooled clinical signs & tests, AUC=0.556 and iii) pooled DMs globally, AUC=0.853. In roccomp analysis for differences in AUC values: i) and iii) p<0.0001; between ii) and iii) p<0.0001.

CONCLUSION

As confirmed by ROC and HSROC analysis, the new DMs with triage mode proved to be far superior in their DA for AcDi as compared to both symptoms and signs & tests. In the lack of earlier studies, these data report the first evidence that the DM including triage at an ED could improve the detection of AcDi.

Many diagnostic tools for appendicitis: a scoping review

BACKGROUND

We aimed to provide an overview of all diagnostic tools developed to diagnose appendicitis with their reported accuracy and to further characterize these including their need for diagnostic equipment.

METHODS

This scoping review followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews and a protocol was registered at Open Science Framework. We performed a systematic literature search in PubMed, Embase, China National Knowledge Infrastructure, Índice Bibliográfico Espanhol de Ciências da Saúde, and Latin American and Caribbean Health Sciences Literature. We included original articles of all languages with the purpose to derive an accessible diagnostic tool. We extracted data regarding study- and diagnostic tool characteristics, and the accuracy of each diagnostic tool.

RESULTS

The search led to 6419 records, where 74 studies were included, yielding 82 diagnostic tools reported in seven different languages. Among these tools, 35% included patient characteristics, 85% symptoms, 93% physical examinations, 37% vital signs, 78% laboratory values, and 16% imaging. Among the diagnostic tools, 35% relied on a medical doctor/surgeon with access to a laboratory, and six diagnostic tools did not require a bedside medical doctor/surgeon. The median positive predictive value, negative predictive value, sensitivity, and specificity across diagnostic tools were 91%, 94%, 89%, and 86%, respectively.

CONCLUSIONS

We identified 82 diagnostic tools that most frequently were based on symptoms and physical examinations. Most diagnostic tools relied on a medical doctor/surgeon with access to laboratory values. The accuracy was high across the diagnostic tools.

History-taking, Clinical Signs, Tests and Scores for Detection of Non-organic Dyspepsia (NOD) Among Patients With Acute Abdominal Pain (AAP)

BACKGROUND/AIM

The diagnostic accuracy of history-taking, clinical signs and tests and diagnostic scores (DSs) for patients with non-organic dyspepsia (NOD) have been rarely evaluated.

PATIENTS AND METHODS

A cohort of 1333 patients presenting with acute abdominal pain (AAP) were studied, including 50 patients with confirmed NOD. The most significant diagnostic variables (in multivariate logistic regression analysis) were used to construct six different DS models and their diagnostic accuracy was compared with clinical symptoms and signs and tests. Meta-analytical techniques were used to detect the summary sensitivity (Se) and specificity (Sp) estimates for each data set (symptoms, signs and tests as well as DS models).

RESULTS

In hierarchical summary receiver operating characteristic (HSROC) analysis, the area under curve (AUC) values for i) symptoms ii) signs and tests iii) DS were as follows: i) AUC=0.608 [95% confidence interval (CI)=0.550-0.666]; ii) AUC=0.621 (95% CI=0.570-0.672) and iii) AUC=0.877 (95% CI=0.835-0.919). The differences between these AUC values (roccomp analysis) are as follows: between i) and ii) p=0.715; between i) and iii) p<0.0001; between ii) and iii) p<0.0001.

CONCLUSION

The present study is the first to provide evidence that the DS could be used in diagnosis of NOD. The major advantage of our DS is that this model does not need radiology or endoscopy to reach high diagnostic accuracy.

A Diagnostic Score (DS) in the Difficult Diagnosis of Non-specific Abdominal Pain (NSAP)

BACKGROUND/AIM

The diagnostic scores (DSs) for patients with non-specific abdominal pain (NSAP) have been rarely evaluated.

PATIENTS AND METHODS

In the NSAP study group there were 614 patients (268 females and 346 males) versus 719 patients in the non-NSAP group including 368 females and 351 males. The clinical symptoms (n=22), signs and tests (n=14) and laboratory analyses (n=3) were recorded in each patient. Meta-analytical techniques were used to detect the summary sensitivity (Se) and specificity (Sp) estimates for each data set (symptoms, signs and tests as well as DS models).

RESULTS

In receiver operating characteristic (ROC) analysis, the area under curve (AUC) values for i) symptoms ii) signs and tests and iii) DS were as following: i) AUC=0.542 (95% CI=0.512-0.572); ii) AUC=0.625 (95% CI=0.550-0.700), and iii) AUC=0.874 (95% CI=0.850-0.898). The differences between these AUC values are as following: between i and ii, p=0.097; between i and iii, p<0.0001 and between ii and iii, p<0.0001.

CONCLUSION

This is the first study to provide evidence that DS may help in the difficult diagnosis of NSAP.