Research ArticleClinical Studies
Open Access

Accuracy of Prediction Models in Diagnosis of Acute Diverticulitis

MAARET ESKELINEN, JUKKA PULKKINEN, TUOMAS SELANDER, KARI SYRJÄNEN and MATTI ESKELINEN
In Vivo November 2023, 37 (6) 2597-2608; DOI: https://doi.org/10.21873/invivo.13367

Abstract

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.

Key Words:

Of all AAP (acute abdominal pain) patients referred to the emergency department (ED), acute diverticulitis (AcDi) is one of the most common diagnoses and among the main gastroenterological causes of hospitalization in the Western world (1-9), thus requiring more attention. AcDi is portrayed by an acute inflammation of the colonic diverticulum leading to focal necrosis of the diverticular wall, with a potential to result in a microscopic or macroscopic perforation of a diverticulum. In addition, the diverticulum itself and the adjacent inflamed colon could obstruct, leading to the full-blown clinical picture of a large bowel obstruction.

Because of these divergent clinical presentations, descriptions of the clinical appearances of AcDi in the literature are somewhat fragmentary and even contradictory. Although lower abdominal (Lab) pain, increased body temperature (Tax), abdominal guarding and absence of vomiting are suggested to predict AcDi in some reports (3-5), international guidelines for diagnosis and treatment of AcDi do not advocate basing the diagnosis of AcDi on only the clinical symptoms, signs, and laboratory tests of the patient (1-9). Therefore, some authors have tried to develop diagnostic models (DMs) to enhance the diagnostic accuracy (DA) of AcDi, but these DMs have not yet been validated and the most studies are based on a small sample size (10-15). In addition, some of the introduced DM scores, e.g., those of Andeweg et al. (12) and Bolkenstein et al. (14) include C-reactive protein (CRP) level (12) or leucocyte count (Leuc) (14). Although their proposed DMs could slightly enhance the DA in ruling out non-AcDi diagnoses, the laboratory tests included in these DMs are a disadvantage, because many of these are not available at the ED settings, thus precluding an accurate prediction of the disease progression by the emergency physicians.

The present study is the first where the DA of the i) symptoms, ii) signs & tests, as well three DM score models (without triage, with one triage and with two triages) were compared among AcDi and non-AcDi patients, included in a cohort of 1,333 AAP patients (16, 17).

Patients and Methods

The AcDi study group comprised of 30 patients with AcDi and 1,303 patients in the non- AcDi group. The clinical symptoms (n=22), signs and tests (n=14) as well as laboratory analyses (n=3) were recorded for each patient. The diagnosis of AcDi was confirmed by considering all clinical history-taking details, clinical findings, and results of the laboratory tests together and following the diagnostic criteria of AAP (acute abdominal pain) and AcDi. The triage was performed at arrival to ED unit (triage I) and at follow-up (triage II) before final decision. The triage included a suggestion of possible diagnosis for AAP patient.

DM models. In a multivariate logistic (stepwise) regression analysis SPSS software was used (SPSS statistics 26.0.0.1; IBM, Armonk, NY, USA) and the clinical features shown in Table I and Table II were included in the stepwise analysis as binary data e.g., AcDi=1 and other diagnosis of AAP=0. Using the coefficients of the stepwise model, a DM was built and its predictive value for AcDi was estimated.

View this table:
Table I.

The clinical history of the acute diverticulitis (AcDi) versus other causes of acute abdominal pain.

View this table:
Table II.

The clinical signs and investigations of the acute diverticulitis (AcDi) patients versus other causes of acute abdominal pain.

1. The DM for AcDi without triage (PE=positive endpoint and NE=negative endpoint):

DM=−0.75×Progression of pain (PE=1, NE=0)-0.92×Vomiting

(PE=1, NE=0)+2.35×Tenderness (PE=1, NE=0)+2.37×Rigidity

(PE=1, NE=0)-1.18×Leuc (PE=1, NE=0)-4.79.

2. The DM for AcDi with triage at ED (Triage I):

DM=−1.10×Progression of pain (PE=1, NE=0)-0.79×Previous similar pain (PE=1, NE=0)+1.57×Tenderness

(PE=1, NE=0)+1.22×Guarding (PE=1, NE=0)+1.49×Rigidity

(PE=1, NE=0)-1.51×Leuc (PE=1, NE=0)+4.44×Triage I

(PE=1, NE=0)-4.34.

3. The DM for AcDi with triage at ED (triage I) and at follow-up (triage II):

DM=−1.11×Progression of pain (PE=1, NE=0)-0.73×Vomiting

(PE=1, NE=0)-1.09×Bowels (PE=1, NE=0)+1.66×Tenderness

(PE=1, NE=0)+0.96×Guarding (PE=1, NE=0)+1.57×Rigidity

(PE=1, NE=0)-0.65×Body temperature (PE=1, NE=0)-1.41×Leuc

(PE=1, NE=0)+3.72×Urine (PE=1, NE=0)+4.07×Triage I

(PE=1, NE=0)+1.16×Triage II (PE=1, NE=0)-8.26.

Statistical analysis. STATA/SE version 17.1 (StataCorp, College Station, TX, USA) and SPSS statistics 26.0.0.1 (IBM, Armonk, NY, USA) was used for statistical analysis. The statistical tests presented were two-sided, and p-values <0.05 was considered statistically significant. Using 2×2 tables, sensitivity (Se) and specificity (Sp) with 95% confidence intervals (95% CI) for each clinical history-taking variables, findings or tests were determined. Meta-analytical technique (metaprop) was used to create separate forest plots for Se and Sp for each set of data, including each diagnostic variable. Roccomp test (STATA) was used to compare the AUC values of the SROCs between the 3 diagnostic sets (history-taking, clinical signs, DMs). Conventional ROC analysis was used to find the optimal cut-off values for each of the DMs.

Results

The history taking in AcDi. The overall Se of the clinical symptoms for AcDi was 61% (95% CI=45-76%) (Figure 1). The Se was higher than 61% for 10 of the symptoms. The six most sensitive clinical history-taking variables (relieving factors, vertigo, jaundice, micturition, drugs for abdominal pain, use of alcohol) showed 93-100% Se in diagnosis of AcDi (Figure 1). The overall Sp of the history-taking for detecting AcDi was only 39% (95% CI=25-53%) (Figure 2). Altogether, 11 symptoms showed Sp higher than 39%. The six most specific symptoms of AcDi (location of initial pain, location of pain at diagnosis, aggravating factors, previous similar pain, previous indigestion, bowels, previous abdominal diseases) showed 73-85% Sp (Figure 2).

Figure 1.
Figure 1.

Sensitivities of history-taking in acute diverticulitis (AcDi) (random-effects model). ES: Estimated sensitivity; CI: confidence interval.

Figure 2.
Figure 2.

Specificities of history-taking in acute diverticulitis (AcDi) (random-effects model). ES: Estimated specificity; CI: confidence interval.

Examination and tests in AcDi. The overall Se of the signs & tests for AcDi was 41% (95% CI=25-59%) (Figure 3), and 9 signs & tests had Se exceeding 41%. The six most accurate signs & tests (colour, scar, rebound, guarding, rectal digital tenderness, urine) showed 60-100% Se (Figure 3). The overall Sp of the signs & tests was 65% (95% CI=48-80%) (Figure 4), while 9 signs & tests showed Sp higher than 65%. The five most specific signs & tests (abdominal movement, distension, mass, Murphy’s positive, renal tenderness) showed 91-98% Sp (Figure 4).

Figure 3.
Figure 3.

Sensitivities of signs and tests in acute diverticulitis (AcDi) (random-effects model). ES: Estimated sensitivity; CI: confidence interval.

Figure 4.
Figure 4.

Specificities of clinical signs and tests in acute diverticulitis (AcDi) (random-effects model). ES: Estimated specificity; CI: confidence interval.

DM without triage in AcDi. The most prominent features of AcDi in stepwise analysis without triage mode were progression of pain, vomiting, tenderness, rigidity and Leuc. The best diagnostic level for DM score without triage [DM I; Se=77%, Sp=73%, efficiency (Eff)=73%] was reached at cut-off level −4.0 for DM (Figure 5 and Figure 6; Table III).

Figure 5.
Figure 5.

Sensitivities of diagnostic scores at six different cut-off levels (DS I-VI). ES: Estimated sensitivity; CI: confidence interval.

Figure 6.
Figure 6.

Specificities of diagnostic scores at six different cut-off levels (DS I-VI). ES: Estimated specificity; CI: confidence interval.

View this table:
Table III.

Diagnostic score models (DM) for acute diverticulitis (AcDi) patients. The DM shown at six different cut-off levels of symptoms, signs, and tests. DM I and II; only symptoms and signs without triage at emergency department (ED). Cut-off levels: DM I=−4.00, DM II=−3.41, DM III and IV; symptoms and signs with triage at ED (triage I). Cut-off levels: DM III=−4.00, DM IV=−3.17. DM V and DS VI; symptoms and signs with triage I and with triage at follow-up (triage II). DM V=−5.00, DM VI=−4.13.

DM with triage I mode in AcDi. The most prominent features of AcDi in stepwise analysis with triage I mode were progression of pain, previous similar pain, tenderness, guarding, rigidity, Leuc and triage I (AcDi versus non-AcDi). The best diagnostic level for DM with triage I mode (DM IV; Se=70%, Sp=88%, Eff=88%) was reached at cut-off level −3.17 for DM (Figure 5 and Figure 6; Table III).

DM with two triage modes (triage I+triage II) in AcDi. The most prominent features of AcDi in stepwise analysis with triage I+II mode were progression of pain, vomiting, bowels, tenderness, guarding, rigidity, body temperature (Tax), Leuc, urine, triage I and triage II (AcDi versus non-AcDi). The best diagnostic level for DM with triage I+II mode (DM VI; Se=83%, Sp=80%, Eff=80%) was reached at cut-off level −4.13 for DM (Figure 5 and Figure 6). The overall Se and Sp of these six DM formulas was 80% (95% CI=70-88%) and 78% (95% CI=71-84%), respectively (Figure 5 and Figure 6). Two of these formulas showed Se ≥80% and four formulas had Sp ≥78%.

ROC, HSROC and AUC values. In the conventional ROC analysis for test optimization and finding optimal cut-off points, the AUC 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. HSROC curves were used to visualise the pooled overall accuracy of the symptoms (Figure 7), signs & tests (Figure 8) and different DM models (Figure 9) in detecting AcDi. In SROC analysis, the AUC values for i) symptoms ii) signs & tests iii) DM were as follows: i) AUC=0.540 (95% CI=0.490-0.590); ii) AUC=0.556 (95% CI=0.496-0.616), and iii) AUC=0.853 (95% CI=0.813-0.893). The differences between these AUC values (roccomp analysis) are as follows: between i) and ii) p=0.664; between i) and iii) p<0.0001; between ii) and iii) p<0.0001.

Figure 7.
Figure 7.

Hierarchical summary receiver operating characteristic (HSROC) curve of the history-taking in acute diverticulitis (AcDi).

Figure 8.
Figure 8.

Hierarchical summary receiver operating characteristic (HSROC) curve of the clinical signs and tests in acute diverticulitis (AcDi).

Figure 9.
Figure 9.

Hierarchical summary receiver operating characteristic (HSROC) curve of the six diagnostic score models (DMs).

Discussion

Prompted by the difficulty of AcDi diagnosis among the AAP patients, some authors have attempted to develop DMs in order to enhance the DA, but these DMs remain still unvalidated and most are based on studies with a small sample size (10-15). Lameris et al. (10) recorded clinical picture in AAP patients presenting at the ED and identified features associated with AcDi. They developed a DM based on Lab pain, vomiting, and increased CRP. The calculated AUC values of the individual clinical features ranged from 0.51 for diarrhea to 0.74 for CRP. Unfortunately, they did not provide a HSROC analysis for their DM.

Toorenvliet et al. (11) assessed 57 patients with AcDi diagnosis to compare the DA of clinical evaluation, abdominal ultrasound (AUS) and computed tomography (CT). They concluded that the DA of the clinical diagnosis for AcDi is low, because the tentative diagnosis of AcDi changed in 37% of their AcDi patients. Although AUS and CT show a higher DA in AcDi, these procedures rarely changed the initial management proposal for the AcDi diagnosis in their study (11). Unfortunately, these authors did not provide any AUC values for the diagnostic tests applied.

Andeweg et al. (12) recorded the clinical picture of their AAP patients seen on the ED attempting to find predictors for AcDi and to create a DM. They proposed a DM including 1) the number of previous episodes of AcDi, 2) aggravation of pain, 3) absence of vomiting, 4) Lab pain and 5) CRP >50 mg/l and developed a nomogram using these features. They calculated the AUC values of the individual clinical features in all patients ranging between 0.52 for sex and 0.73 for Lab pain. They did not perform HSROC analysis for their DM or nomogram.

Jamal Talabani et al. (13) investigated the DA of AcDi among AAP patients admitted to an ED in Norway. The probability of AcDi was assessed by the examining doctor, using a scale from 0 (zero probability) to 10 (100 % probability). They reached the AUC values for their probability score; AUC=0.95 for AcDi patients <65 years and AUC=0.86 for older AcDi patients. The AUC values for the laboratory tests in AcDi are ranging from 0.59 for Leuc to 0.83 for CRP. Again, HSROC analysis with AUC values available of the individual clinical features was not made. Albeit that the authors admit that there could be a selection bias due to the ED doctors’ awareness of the study, this study demonstrates that clinical evaluation based on symptoms, signs and tests on admission provides a high DA in AcDi patients.

Bolkenstein et al. (14) performed a retrospective study including AcDi patients presented at ED. Clinical signs and laboratory parameters were collected and they developed a DM to distinguishing complicated AcDi from uncomplicated AcDi patients. Their DM score included abdominal guarding, CRP level and Leuc. Although, proposed DM could slightly enhance the DA in ruling out complicated AcDi, the laboratory tests needed in this DM includes a significant limitation. Sigurdardottir et al. (15) investigated patients with clinical suspicion of AcDi, proposing a DM for AcDi that includes sex, age, urinary symptoms, vomiting, body temperature, CRP, and Lab tenderness. AUC for this DM was 0.82, with the AUC values for the individual clinical features varying from 0.50 for obstipation to 0.65 for CRP (15).

The present study is a comprehensive analysis of the DA in AcDi, by taking into account the role of i) history-taking, ii) examination and iii) laboratory analyses, all used to build up a diagnostic score model for AcDi. As to the symptoms, a special meaning should have abdominal pain, which in AcDi tends to begin in the Lab and often remains in the Lab until the AcDi patient is seen at the hospital. In our study, 40% (12/30) of the AcDi patients had location of initial pain and pain at diagnosis at Lab. The pain of AcDi is sometimes quite severe, and in our study 73% (22/30) of AcDi patients had moderate or intolerable pain. One variable important in distinguishing AcDi from peritonitis (Perit) is that the pain in AcDi is mostly steady or intermittent as in 67% (20/30) of patients in this study, whereas this type of pain is rare in Perit. In 20% (6/30) of AcDi patients, the pain is aggravated by movement and this variable does not distinguish AcDi from the Lab diseases such as acute appendicitis or acute small bowel obstruction.

Nausea and vomiting are usually lacking in AcDi. However, the DA of these symptoms has not been considered in AcDi before. In our study, 43% (13/30) of the AcDi patients had nausea and 27% (8/30) had vomiting. Most patients with AcDi have not noticed any abnormality of bowel habits (bowels normal in 26/30) or micturition on admission (micturition normal in 29/30). AcDi tends to be associated with poor appetite. In this investigation, 73% (22/30) of the AcDi patients had positive clinical history of poor or lacking appetite.

The patient with AcDi is unlikely to have a fully non-mobile abdomen at abdominal palpation (Ap). This happens only in about every 13th patient with AcDi in our study (abdominal movement normal 93.3%) (28/30). With regard to tenderness at Ap, half of AcDi patients have Lab tenderness (15/30, 50%), whereas rebound was present in 60% (18/30) of the AcDi patients. Importantly, rigidity was present in half (50%) of the AcDi patients and this is a difference from Perit, where rigidity is a rule rather than exception. On abdominal auscultation, about one fourth (7/30, 23%) of the AcDi patients in our study had abnormal bowel sounds, while the rest of the AcDi patients had bowel sounds, indistinguishable from normal sounds.

In the severity grading of AcDi, there are some formulas that have been used as predictors of organ failure, complications, and survival in AcDi patients (19-21). However, the DMs for patients with AcDi have been rarely evaluated and the DMs with the triage performed at patients’ arrival to the ED (triage I) and at follow-up (triage II) before final decision are even more rare.

Meklin et al. (18) have recently introduced HSROC analysis in distinguishing DA of traditional and new-generation fecal immunochemical tests used in colorectal cancer screening. Their experience also suggests that HSROC could assist the clinician in differentiating non-specific abdominal pain from acute appendicitis (16, 17) and other causes of AAP, and improve the DA in following AAP patients; acute cholecystitis, acute renal colic, acute small bowel obstruction, non-organic dyspepsia, and acute pancreatitis (22-29). The HSROC analysis displays Se as a function of the false positive (FP) rate (1- Sp). HSROC curve for clinical symptoms in AcDi detection almost parallels the diagonal reference line (AUC=0.5) with low AUC value (AUC=0.540) (Figure 7). The DA of the signs & tests is practically similar as that of the clinical symptoms (AUC=0.556) (Figure 8). These data implicate that neither the clinical symptoms nor signs & tests are helpful in diagnosis of AcDi in an ED setting.

The present results clearly confirm that the DMs are highly accurate measures in diagnosing AcDi. As shown in Figure 9, the HSROC curve for the pooled DMs (I-VI), is shifted towards the upper left corner, indicating a significantly better diagnostic accuracy AcDi (AUC=0.853) than obtained by the clinical examination alone. The conventional ROC curves were used for selecting the optimal cut-off points for each DM, i.e., the best balance between Se and Sp. In the present series, the DA of the single DM with two triage (AUC=0.926) was the highest of all DMs, far exceeding that (AUC=0.853) of the HSROC analysis for the pooled DMs in diagnosis of AcDi. Estimating the clinical usefulness of DM formulas for AcDi, we have calculated the usefulness index (UI) (30) for six different formulas. The UI is defined as d×(d-r), where d is the incidence of the finding in the disease (=Se) and r is the incidence of the finding in a reference population (1-Sp). It runs coherently from −1 to 1 and tests where the UI is over 0.35 are regarded as useful (30-33). In the UI analysis of this study, the UI values for the individual DM formulas ranged from 0.357 for DM II to 0.59 for DM V and about 0.45 median UI values were reached in different DM formulas. Taken together, the DM for AcDi performed well considering the simple nature of its structure, but the present AcDi cohort is far too small to draw final conclusions. The results are encouraging enough to warrant a new prospective study with a larger number of AcDi patients, to assess the validity of this DM and to provide additional data for possible revisions of these models.

Conclusion

As confirmed by ROC and HSROC analysis, the new DMs with the triage mode proved to be far superior in their DA for AcDi 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.

Acknowledgements

The study was funded by the North Savo Regional Fund (Pohjois-Savon Maakuntarahasto).

Footnotes

  • Authors’ Contributions

    All Authors contributed to the collection and analysis of data, drafting, and revising the manuscript, read and approved the final article.

  • Conflicts of Interest

    The Authors report no conflicts of interest or financial ties to disclose.

  • Received June 29, 2023.
  • Revision received July 27, 2023.
  • Accepted August 28, 2023.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY-NC-ND) 4.0 international license (https://creativecommons.org/licenses/by-nc-nd/4.0).

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Accuracy of Prediction Models in Diagnosis of Acute Diverticulitis
MAARET ESKELINEN, JUKKA PULKKINEN, TUOMAS SELANDER, KARI SYRJÄNEN, MATTI ESKELINEN
In Vivo Nov 2023, 37 (6) 2597-2608; DOI: 10.21873/invivo.13367
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