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. 2024 Mar 25;11(Suppl 1):S65–S75. doi: 10.1093/ofid/ofad652

Evaluation of Fecal Inflammatory Biomarkers to Identify Bacterial Diarrhea Episodes: Systematic Review and Protocol for the Enterics for Global Health Shigella Surveillance Study

Courtney Babb 1,#,, Henry Badji 2,#, Md Taufiqur Rahman Bhuiyan 3,#, Jennifer Cornick 4,5,#, Sonia Qureshi 6,#, Catherine Sonye 7,#, Wagner V Shapiama Lopez 8,#, Mehreen Adnan 9, Hannah E Atlas 10, Kehkashan Begum 11, Stephanie A Brennhofer 12, Bubacarr E Ceesay 13, Abdoulie K Ceesay 14, Nigel A Cunliffe 15, Paul F Garcia Bardales 16, Shahinur Haque 17, Bri’Anna Horne 18,19, M Jahangir Hossain 20, Junaid Iqbal 21, Md Taufiqul Islam 22, Sadia Islam 23, Farhana Khanam 24, Karen L Kotloff 25,26, Thandizo Malemia 27, Katia Manzanares Villanueva 28, Gertrude Malola Million 29, Vitumbiko Munthali 30, John Benjamin Ochieng 31, Billy Ogwel 32, Maribel Paredes Olortegui 33, Richard Omore 34, Patricia B Pavlinac 35, James A Platts-Mills 36, Khandra T Sears 37,38, Ousman Secka 39, Sharon M Tennant 40,41, Pablo Peñataro Yori 42, Mohammad Tahir Yousafzai 43, Khuzwayo C Jere 44,45,46,#, Margaret N Kosek 47,#, Stephen Munga 48,#, Usman N Ikumapayi 49,#, Firdausi Qadri 50,#, Farah Naz Qamar 51,#, Elizabeth T Rogawski McQuade 52,#,✉,5
PMCID: PMC10962755  PMID: 38532957

Abstract

Background

The measurement of fecal inflammatory biomarkers among individuals presenting to care with diarrhea could improve the identification of bacterial diarrheal episodes that would benefit from antibiotic therapy. We reviewed prior literature in this area and describe our proposed methods to evaluate 4 biomarkers in the Enterics for Global Health (EFGH) Shigella surveillance study.

Methods

We systematically reviewed studies since 1970 from PubMed and Embase that assessed the diagnostic characteristics of inflammatory biomarkers to identify bacterial diarrhea episodes. We extracted sensitivity and specificity and summarized the evidence by biomarker and diarrhea etiology. In EFGH, we propose using commercial enzyme-linked immunosorbent assays to test for myeloperoxidase, calprotectin, lipocalin-2, and hemoglobin in stored whole stool samples collected within 24 hours of enrollment from participants in the Bangladesh, Kenya, Malawi, Pakistan, Peru, and The Gambia sites. We will develop clinical prediction scores that incorporate the inflammatory biomarkers and evaluate their ability to identify Shigella and other bacterial etiologies of diarrhea as determined by quantitative polymerase chain reaction (qPCR).

Results

Forty-nine studies that assessed fecal leukocytes (n = 39), red blood cells (n = 26), lactoferrin (n = 13), calprotectin (n = 8), and myeloperoxidase (n = 1) were included in the systematic review. Sensitivities were high for identifying Shigella, moderate for identifying any bacteria, and comparable across biomarkers. Specificities varied depending on the outcomes assessed. Prior studies were generally small, identified red and white blood cells by microscopy, and used insensitive gold standard diagnostics, such as conventional bacteriological culture for pathogen detection.

Conclusions

Our evaluation of inflammatory biomarkers to distinguish diarrhea etiologies as determined by qPCR will provide an important addition to the prior literature, which was likely biased by the limited sensitivity of the gold standard diagnostics used. We will determine whether point-of-care biomarker tests could be a viable strategy to inform treatment decision making and increase appropriate targeting of antibiotic treatment to bacterial diarrhea episodes.

Keywords: diagnostic, diarrhea, inflammatory biomarker, Shigella, systematic review


Fecal inflammatory biomarkers may accurately identify bacterial diarrheal episodes, which could inform antibiotic treatment decisions. Prior research was limited by using insensitive diagnostics. We will evaluate 4 inflammatory biomarkers in stool from children with diarrhea in the EFGH study.


Bacterial pathogens, such as Shigella and diarrheagenic Escherichia coli, are leading causes of diarrhea among children <5 years of age in low-resource settings. Appropriate antibiotic treatment of bacterial diarrhea episodes can limit morbidity and mortality [1, 2]. In a large, multicountry trial of azithromycin for children with watery diarrhea and dehydration, severe stunting, or moderate wasting, the benefit of azithromycin was observed primarily in children with a bacterial cause of diarrhea, namely Campylobacter, typical enteropathogenic E coli (EPEC), heat-stable enterotoxigenic E coli (ST-ETEC), Salmonella, Shigella, and Vibrio cholerae [3]. Furthermore, antibiotic treatment of Shigella-attributed moderate-to-severe diarrhea (MSD) was associated with improved short-term linear growth in the Global Enteric Multicenter Study (GEMS) [4], and antibiotic treatment of MSD was associated with lower risk of persistent diarrhea in the Vaccine Impact on Diarrhea in Africa (VIDA) study [5].

Targeting antibiotics to children with bacterial diarrhea is needed to limit antibiotic overuse and development of antimicrobial resistance. Current treatment guidelines take a syndromic approach, recommending antibiotic treatment for dysentery or presumed cholera [6], which comprise a small proportion of all bacterial diarrhea episodes. In the Etiology, Risk Factors, and Interactions of Enteric Infections and Malnutrition and the Consequences for Child Health and Development (MAL-ED) and VIDA studies, caregivers reported blood in stool for only 14.5% [7] and 43.8% [8] of shigellosis episodes, respectively. Of all bacterial diarrhea episodes in MAL-ED, caregivers reported blood in 10.4%. This suggests that most cases of shigellosis and other bacterial diarrhea episodes are missed according to current guidelines. Furthermore, younger children, who are most likely to die or be hospitalized from diarrhea and could significantly benefit from treatment, are less likely to present with dysentery [8–10]. Watery bacterial episodes are difficult to distinguish clinically, and prediction scores for specific etiologies that have been developed tend to be driven more heavily by epidemiologic characteristics (including age and season) than the presence of symptoms. For example, a clinical prediction score for Shigella developed in MAL-ED classified that nearly all episodes among children >18 months should be treated, and identified only a few episodes from 6–18 months that should be treated depending primarily on the presence of blood in addition to other symptoms [7]. While it is an important improvement over using the presence of blood alone to identify shigellosis, this score still only identified half of Shigella-attributed episodes [7].

In the absence of readily available point-of-care (POC) diagnostics, measurement of inflammatory biomarkers (ie, those indicative of leukocytes and/or erythrocytes in stool) could substantially improve clinical prediction scores to identify the subset of watery diarrhea episodes that would benefit from antibiotic therapy. Shigella secretes virulence factors that have enterotoxic activity and allow Shigella to invade the colonic epithelium, inducing an inflammatory response [11]. Campylobacter, Salmonella, enteroaggregative E coli, EPEC, and enteroinvasive E coli are also inflammatory and can cause invasive disease [12]. Several studies beginning in the 1970s found that the presence of red blood cells (RBCs) and white blood cells (WBCs) on stool microscopy was more common in shigellosis and other bacterial diarrhea episodes compared to viral episodes [13–18], and immunoassays for biomarkers of leukocytes such as lactoferrin [19–22] and calprotectin [23–27] have also been assessed to distinguish diarrhea etiology. If these markers prove to be sufficiently predictive of watery bacterial diarrhea episodes, inflammatory biomarker stool tests could be adapted into lateral flow assays, which would be readily deployable at the POC to inform antibiotic treatment, with limited demand for staff training or laboratory infrastructure.

The Enterics for Global Health (EFGH) Shigella surveillance study offers an ideal platform to further investigate novel strategies to identify bacterial diarrhea given its rich dataset and sample archive among a geographically diverse sample of children with diarrhea. In this article, we systematically review the literature of studies that assessed the sensitivity and specificity of fecal inflammatory biomarkers to identify bacterial diarrhea and describe our proposed methods for characterizing the performance of inflammatory biomarker tests to identify watery shigellosis and other bacterial diarrhea episodes in EFGH.

METHODS

We searched PubMed and Embase databases for studies published after 1 January 1970, using a combination of search terms (Supplementary Appendix) to capture bacterial diarrhea, fecal inflammatory biomarkers, and diagnostic studies. Included studies were published in English and conducted in individuals of all ages with bacterial diarrhea in any setting. Studies of natural history, blood biomarkers, diarrheal illnesses related to chronic diseases, asymptomatic infections, and animals were excluded. Review articles, case reports, and studies without sufficient data for extraction were also excluded.

Study selection was conducted using Covidence software [28]. Screening of titles and abstracts and the full text review was performed independently by 2 reviewers (C. B., S. Q., H. B., W. V. S. L., or E. T. R. M.). Disagreements were resolved by a third reviewer. Enrollment dates, study location, number of participants with diarrhea and specific pathogens, diagnostic gold standard used, biomarkers assessed, and the associated sensitivities and specificities for the identification of Shigella and a combined bacterial diarrhea outcome if available (eg, all bacteria, invasive bacteria, or a group of specific pathogens; Supplementary Table 1), were extracted from included studies by 1 of the above authors and checked by a second author (C. B. or E. T. R. M.). This review is registered with PROSPERO (CRD42023409479).

If a combined bacterial diarrhea outcome was not reported, we summed counts of individual bacterial outcomes and calculated sensitivity for the group of bacteria. When specificity was not directly provided, we calculated specificity as 1-sensitivity for the detection of negative outcomes such as no pathogen detected, viruses or parasites detected, or noninflammatory or noninvasive bacteria detected (Supplementary Table 1). We categorized studies by setting and age group. The quality of studies was assessed using Quality Assessment of Diagnostic Accuracy Studies (QUADAS) criteria [29] (Supplementary Table 2).

RESULTS

After screening 3875 titles and abstracts, and reviewing 93 full texts, 49 studies met inclusion criteria (Supplementary Figure 1). The enrollment period of included studies ranged from 1975 to 2020 (Supplementary Table 1). Thirty-four (69%) studies included children, 12 studies (25%) included adults only, and age was unknown for 3 (6%) studies. Twenty-four (49%) studies were conducted in low- and middle-income countries (LMICs) compared to 25 (51%) in high-income countries. Four studies were of travelers or the military, and 1 study included experimentally infected healthy volunteers. Of the 49 studies included, 39 (80%) assessed fecal leukocytes, 26 (53%) assessed RBCs/occult blood, 13 (27%) assessed lactoferrin, 8 (16%) assessed calprotectin, and 1 (2%) assessed myeloperoxidase (MPO). Shigella was assessed in 21 (43%) studies, and other bacterial outcomes were assessed in 40 (82%) studies. Eighteen (36%) studies included children in LMICs, of which 17 (35%) examined leukocytes, 10 (20%) examined RBCs/occult blood, and 5 (10%) examined other biomarkers. Most studies (n = 43 [88%]) used traditional bacterial culture alone as the gold standard diagnostic to identify bacterial causes of diarrhea. Three studies (6%) used culture and polymerase chain reaction (PCR), and 3 studies (6%) used PCR alone.

The median sensitivity of fecal leukocytes for identifying Shigella was 78% (interquartile range [IQR], 68%–94%) and 45% (IQR, 33%–69%) for identifying combined bacterial outcomes (Table 1). Specificity for Shigella was assessed in only 3 studies and ranged from 61% to 74%. Specificity for combined bacterial outcomes varied depending on the nonbacterial outcomes considered, with a median specificity of 85% (IQR, 75%–90%). For RBCs/occult blood, median sensitivity for Shigella was 70% (IQR, 51%–84%) and for combined bacterial outcomes was 48% (IQR, 37%–74%; Table 2). Specificity for Shigella was estimated in 1 study at 61% [30], and median specificity for combined bacterial outcomes was 69% (IQR, 63%–85%). Sensitivity of lactoferrin for Shigella was estimated in 4 studies and ranged from 61% to 100% (Table 3). Median sensitivity and specificity of lactoferrin for combined bacterial outcomes was 88% (IQR, 74%–94%) and 51% (IQR, 26%–69%), respectively. For calprotectin, sensitivity for Shigella was 78% in 1 study. Median sensitivity and specificity of calprotectin for combined bacterial outcomes was 86% (IQR, 76%–93%) and 69% (IQR, 41%–87%), respectively. The 1 study that assessed MPO estimated sensitivity of 78% and specificity of 57% for combined bacterial outcomes [31]. In the 2 studies that assessed both lactoferrin and calprotectin, sensitivity for combined bacterial outcomes was higher for calprotectin [26, 31]. Specificity was not consistently higher or lower between the 2 biomarkers (Table 3) [26, 31].

Table 1.

Sensitivity and Specificity of Fecal Leukocytes to Identify Shigella and Other Bacterial Causes of Diarrhea in Systematically Reviewed Studies by Setting and Age of Included Individuals (n = 39 Studies)

Setting and Study Study Location WBC Cutoff (per HPFa) No. With Diarrhea No. With Shigella Sensitivity for Shigella, % Specificity for Shigella, % No. With Combined Bacterial Outcome Sensitivity for Combined Bacterial Outcome, % Specificity for Combined Bacterial Outcomeb, %
LMIC children (n = 17)
 Bardhan 2000 [43] Bangladesh >20 1008 205 63 86–96
 Beltinger 1997 [44] Bangladesh >20 304 38 76 54 67 87
 Bodhidatta 2002 [45] Thailand >10 623 56 83
 Chang 2017 [46] China ≥5 680 1 100 215 23 94
 Huicho 1993 [16] Peru >5 446 10 70 58 36 82–88
 Huicho 1997 [21] Peru NS 125 6 29 69 60
 Ismail 1994 [30] Indonesia >5 619 44 68 61 60 52 60
 Jindal 1991 [47] India >10 400 6 67 115 28 88
 Khan 2006 [17] Bangladesh >20 843 454 71 62
 Korzeniowski 1979 [14] USA, Brazil >10/LPF 101 29 95 85
 McNeely 1996 [48] Mexico >5 1040 143 173 42 76
 Mercado 2011 [18] Peru >10 935 257 14 90
 Mshana 2010 [49] Uganda NS 226 14 90 47
 Nordlander 1990 [50] Cambodia >5 50 2 100 70–100
 Patwari 1993 [51] India >0 533 17 60 27 90
 Sebodo 1978 [52] Indonesia NS 92 6 50 75
 Venkataraman 2003 [53] India >3 262 27 42 45 79
LMIC adults only or unknown (n = 3)
 Hossain 1991 [15] Bangladesh >25 11358 3895 80 74
 Pender 2022 [54] Thailand, Nepal NS 453 34c 565c 85 24
 Wang 2014 [55] China NS 424 90c 176c 99 97
HIC children (n = 11)
 Alvarado 1983 [56] UK ≥2 376 34 91 82–100
 Alzaher 2022 [57] Saudi Arabia >0 1985 84 1766 34 78
 Ascher 1991 [58] USA >5 180 8 24 83 96
 Caprioli 1996 [59] Italy NS 618 2 168 57–58 73–88
 Denno 2005 [60] USA >0 226 2 12 45 96
 DuBois 1988 [61] USA >2 69 12 83 25 82 83
 Fan 1993 [62] USA NS 1031 20 50 55 29 93
 Koplan 1980 [63] USA NS 27 13 52 36 69
 McIver 2001 [64] Australia >0 412 30 43 87
 Paccagnini 1987 [13] Italy >5 337 62 32–54 90
 Park 2019 [31] South Korea >0 62 19c 44 84
HIC adults only (n = 8)
 Bouckenooghe 2000 [65] USA NS 227 56 27 85
 Lai 2016 [66] Taiwan >1 627 3c 187c 45 73
 Lever 2021 [67] UK NS 1450 25 269 32 91
 Loosli 1985 [68] Switzerland NS 119 3 35 86 67
 Miller 1994 [69] USA >0 55 9 95
 Scerpella 1994 [70] USA NS 92 32 72 36 69 89
 Siegel 1987 [71] USA ≥3 113 19 95 54 89 68
 Tribble 2008 [72] USA >0 182 177 33 85

Abbreviations: HIC, high-income country; HPF, high-power field; LMIC, low- and middle-income country; LPF, low-power field; NS, not specified; UK, United Kingdom; USA, United States; WBC, white blood cell count.

aUnless otherwise specified.

bRange provided if specificity was calculated based on multiple negative outcome definitions (see Supplementary Table 1 for negative outcome definitions).

cIncludes detection by polymerase chain reaction; otherwise, detection by culture (see Supplementary Table 1 for details).

Table 2.

Sensitivity and Specificity of Red Blood Cells or Occult Blood in Stool to Identify Shigella and Other Bacterial Causes of Diarrhea in Systematically Reviewed Studies by Setting and Age of Included Individuals (n = 26 Studies)

Setting and Study Study Location RBC Test No. With Diarrhea No. With Shigella Sensitivity for Shigella, % Specificity for Shigella, % No. With Combined Bacterial Outcome Sensitivity for Combined Bacterial Outcome, % Specificity for Combined Bacterial Outcomea, %
LMIC children (n = 10)
 Ashraf 2007 [73] Bangladesh FOBT 594 18 56 73 55 53
 Bardhan 2000 [74] Bangladesh FOBT 1008 205 87 18–80
 Beltinger 1997 [44] Bangladesh FOBT 304 38 82 54 69 66
 Chang 2017 [46] China Micro 680 1 215 13
 Huicho 1993 [16] Peru FOBT 446 10 70 58 43 62–69
 Huicho 1997 [21] Peru FOBT 125 6 29 79 50
 Ismail 1994 [30] Indonesia Micro 619 44 68 61 60 52
 Korzeniowski 1979 [14] USA, Brazil FOBT 101 29 85 89
 McNeely 1996 [48] Mexico FOBT 1040 143 173 79 64
 Patwari 1993 [51] India Micro 533 17 60 32 89
LMIC adults only or unknown (n = 4)
 Pender 2022 [54] Thailand, Nepal Micro 453 34b 565b 37 85
 Wang 2014 [55] China Micro 424 90b 176b 96 97
 Aly 2005 [75] Egypt FOBT 40 20 45 30
HIC children (n = 5)
 Alzaher 2022 [57] Saudi Arabia FOBT 1985 84 1766 48 84
 Ascher 1991 [58] USA FOBT 180 8 24 37 67
 Denno 2005 [60] USA FOBT or Micro 226 2 12 40 96
 Paccagnini 1987 [13] Italy FOBT 337 62 74–83 67
 Park 2019 [31] South Korea FOBT 62 19b 61 82
HIC adults only (n = 7)
 Bouckenooghe 2000 [65] USA FOBT 227 56 30 71
 Lai 2016 [66] Taiwan FOBT 627 3b 187b 53 63
 Lever 2021 [67] UK Micro 1450 25 269 16 94
 Loosli 1985 [68] Switzerland FOBT 119 3 35 80 66
 Scerpella 1994 [70] USA FOBT 92 32 37 36 37 82
 Shastri 2008 [26] Germany FOBT 200 2 107 38 85
 Siegel 1987 [71] USA FOBT 113 19 84 54 87 58
 Tribble 2008 [72] USA FOBT 182 177 42 84

Abbreviations: FOBT, fecal occult blood test; HIC, high-income country; LMIC, low- and middle-income country; Micro, microscopy; RBC, red blood cell count; UK, United Kingdom; USA, United States.

aRange provided if specificity was calculated based on multiple negative outcome definitions (see Supplementary Table 1 for negative outcome definitions).

bIncludes detection by polymerase chain reaction; otherwise, detection by culture (see Supplementary Table 1 for details).

Table 3.

Sensitivity and Specificity of Lactoferrin, Calprotectin, or Myeloperoxidase in Stool to Identify Shigella and Other Bacterial Causes of Diarrhea in Systematically Reviewed Studies (n = 19 Studies)

Setting and Study Study Location Biomarker Cutoff No. With Diarrhea No. With Shigella Sensitivity for Shigella, % Specificity for Shigella, % No. With Combined Bacterial Outcome Sensitivity for Combined Bacterial Outcome, % Specificity for Combined Bacterial Outcome, %
Lactoferrin (n = 13)
 Aly 2005 [75] Egypt 1:50 40 20 80 25
 Ashraf 2007 [73] Bangladesh 1:50 594 18 61 73 52 68
 Bouckenooghe 2000 [65] USA 1:50 227 56 27 79
 Choi 1996 [20] USA 1:50 46 3 28 93 83
 Huicho 1997 [21] Peru 1:50 125 6 29 97 15
 McIver 2001 [64] Australia NS 412 30 95 40
 Mercado 2011 [18] Peru NS 935 200 95
 Miller 1994 [69] USA 1:50 55 9 100 43 60 8
 Park 2019 [31] South Korea 22.8 µg/mL 62 19a 78 71
 Scerpella 1994 [70] USA 1:50 92 32 94 36 94 47
 Shastri 2008 [26] Germany 1:400 200 2 107 78 54
 Tribble 2008 [72] USA 1:50 182 177 93 56
 Venkataraman 2003 [53] India 1:50 262 27 42 83 28
Calprotectin (n = 8)
 Ahn 2020 [76] South Korea 388 mg/kg 400 7a 197a 71 61
 Berger 2010 [77] Unknown >50 µg/mL 168 108 80 89
 Czub 2014 [78] Poland 15 µg/mL 50 21 100 45
 Duman 2015 [23] Turkey 710 mg/L 84 7 78 9 89 76
 Kim 2022 [79] South Korea 815 µg/g 80 16a 75b 40
 Park 2019 [31] South Korea 74.0 µg/g 62 19a 94 39
 Shastri 2008 [26] Germany 14.9 mg/L 200 2 107 83 87
 Sýkora 2010 [27] Czech Republic 103.9 µg/g 66 31 93 88
Myeloperoxidase (n = 1)
 Park 2019 [31] South Korea 4.14 ng/mL 62 19a 78 57

Abbreviations: NS, not specified; USA, United States.

aIncludes detection by polymerase chain reaction; otherwise, detection by culture (see Supplementary Table 1 for details).

bEstimated from manuscript figure.

Different definitions of positive for WBC by microscopy (ranging from >0 to >20 cells per high-power field) and different assays and/or cutoffs for the other biomarkers made it difficult to compare results across studies. Similarly, heterogeneity resulted from differences in the combined bacterial outcome considered and the negative outcome used to calculate specificity. Most studies (n = 43 [88%]) were considered low quality (Supplementary Table 2) due to using bacterial culture, which is an insensitive gold standard diagnostic, particularly for Shigella [7]. Only 12 studies (24%) were conducted among children in LMICs (ie, the target population for EFGH), none of which used molecular diagnostics and only 2 of which assessed either lactoferrin or calprotectin. Only 6 (12%) studies included >1000 individuals with diarrhea.

METHODS IN THE EFGH INFLAMMATORY BIOMARKER SUBSTUDY

We will conduct an inflammatory biomarker substudy in 6 EFGH sites: Bangladesh, Kenya, Malawi, Pakistan, Peru, and The Gambia. The objective of this substudy is to evaluate whether inflammatory biomarkers measured in whole stool can identify the bacterial subset of diarrhea episodes, and shigellosis specifically. The primary EFGH study design is described elsewhere [32].

Sample Collection

Whole stool samples will be collected as soon as possible after enrollment from all enrolled children aged 6–35 months presenting with diarrhea at selected study health facilities. Samples will be collected if they are produced at any time while the participant is present at the enrolling facility or within 24 hours of leaving the enrolling facility. This strategy will increase the yield of whole stool collections since children may not produce stool during the enrollment visit. Study staff will conduct home visits to collect stools produced after leaving the facility. Caregivers will also have the option of returning the whole stool sample to the enrollment facility. In both cases, caregivers of participants who do not produce whole stool at the enrollment visit will be provided with a whole stool collection kit and will be instructed to collect the participant's first stool produced after leaving the enrollment facility. Home visits will only occur during routine working hours. Once retrieved by study personnel (and within 18 hours of stool production), whole stool samples will be placed into a cool box (2°C–8°C) for transportation to the laboratory. The following will be verified during accession: labeling, stool volume, and transport conditions, which include packaging and temperature monitoring using WarmMark (after collection by staff only). Whole stool will be aliquoted into up to five 2-mL cryotubes (up to 1 g per cryotube), and frozen at −80°C until testing.

Inflammatory Biomarker Testing

All whole stool samples will be tested for 4 biomarkers: MPO, calprotectin, lipocalin-2 (NGAL), and hemoglobin. These 4 were chosen to capture markers of both leukocytes and erythrocytes since both showed evidence of diagnostic ability in the systematic review. While most prior studies used microscopy to measure fecal WBC and RBC, microscopy would be impractical as a diagnostic in many settings; therefore, we selected protein biomarkers of leukocytes and erythrocytes that either had prior evidence of diagnostic ability (calprotectin, hemoglobin), had strong preliminary data from our prior work (MPO), or represented a novel component of the host immune response, which would limit collinearity between markers (NGAL). Lactoferrin was considered but ultimately rejected given its similarity with calprotectin and the better sensitivity for calprotectin over lactoferrin in studies from the systematic review that assessed both [26, 31]. We will not evaluate systemic biomarkers given the likely infeasibility and unacceptability of collecting blood samples at the POC in most clinical settings.

Each marker, their underlying mechanism of action, and rationale are outlined in Table 4. We will use commercially available enzyme-linked immunosorbent assays (ELISAs) for each biomarker according to their manufacturers’ instruction manuals [33–36]. Biomarker concentrations per gram of stool will be calculated from the raw optical density data using a 4-parameter curve fit to the standards, which will be run in duplicate on every plate. Each plate will also include a high and low concentration control run in duplicate. The analysis of raw optical densities will be centralized using a custom-built R-based Shiny application accessible on the web. The app will allow for monitoring of standards and controls with immediate feedback to the laboratories at each site and real-time quality control (QC) monitoring by a central coordinating team.

Table 4.

Fecal Inflammatory Biomarkers to Be Assessed in the Enterics for Global Health Study

Biomarker Target Description Rationale
Myeloperoxidase Neutrophils A peroxidase enzyme belonging to the heme-containing proteins, produced largely in neutrophils and in smaller quantities in monocytes [80, 81]. MPO is considered a biomarker for neutrophils, inflammatory activity in the gastrointestinal tract, and neutrophil damage [81]. Fecal MPO is a biomarker for IBD [82]. Shigella detections in MAL-ED were associated with increases in MPO, and the association depended on Shigella quantity, such that MPO levels were more highly elevated as the quantity of Shigella increased [10]. Diarrheagenic Escherichia coli, specifically EAEC, EPEC, and ETEC, was also associated with elevated MPO, though to a lesser extent [83].
Calprotectin Neutrophils A protein biomarker of leukocytes and neutrophil damage during intestinal inflammation [23–25]. Fecal calprotectin helps to distinguish between IBD and noninflammatory bowel conditions and monitor IBD activity [84]. Increased levels were observed among those with bacterial compared to viral infections during diarrhea [24]. Calprotectin was elevated in shigellosis at levels higher than other bacterial diarrheas (Clostridioides difficile, Salmonella, Campylobacter, and EIEC) [85].
Lipocalin-2 (NGAL) Neutrophils; enterocyte damage A circulatory protein commonly referred to as neutrophil gelatinase-associated lipocalin (NGAL). NGAL is responsible for the delivery of molecules including steroids, free fatty acids, and hormones to body organs [86]. It is an indicator of innate immunity [87], found in a variety of cells including neutrophils, and possesses antibacterial and anti-inflammatory functions, in addition to providing protection against cell and tissue stress [86]. NGAL is an indicator of enterocyte damage and acute and chronic renal injury [86, 88]. It is also a biomarker for intestinal inflammation and is associated with IBD [87]. Studies in a Shigella murine model demonstrate that sensitivity for Shigella may be higher compared to MPO [89]. Lipocalin-2 decreases rapidly following inflammation [90].
Hemoglobin Fecal occult blood The iron-containing protein present in RBCs responsible for transporting oxygen to organs and tissues [91]. Hemoglobin is a marker of RBCs and its presence in stool indicates the presence of blood. Fecal hemoglobin helps to identify IBD patients with active inflammation [92]. Shigella is the main cause of dysentery among children globally [7]. Presence of RBCs was predictive of shigellosis and other bacterial diarrhea in the systematic review.

Abbreviations: EAEC, enteroaggregative Escherichia coli; EIEC, enteroinvasive Escherichia coli; EPEC, enteropathogenic Escherichia coli; ETEC, enterotoxigenic Escherichia coli; IBD, inflammatory bowel disease; MAL-ED, Etiology, Risk Factors, and Interactions of Enteric Infections and Malnutrition and the Consequences for Child Health and Development; MPO, myeloperoxidase; NGAL, neutrophil gelatinase-associated lipocalin; RBCs, red blood cells.

Detection of Enteric Pathogens

Total nucleic acid will be extracted from rectal swabs from each enrolled participant and tested for enteric pathogens by quantitative PCR (qPCR) using the TaqMan Array Card platform, as described elsewhere in this supplement [37]. Attribution of diarrhea episodes to specific infectious etiologies will be based on the quantities of pathogens detected, and assigned etiologies by qPCR will be considered the “gold standard” diagnostic against which we will compare the inflammatory biomarkers. Specifically, attribution will be assigned if a pathogen is detected at a cycle threshold value below the EFGH and pathogen-specific cutoff, described elsewhere [37]. The definition of etiology will not be dependent on the detection of other pathogens, such that multiple etiologies may be identified for each episode. The presence of co-etiologies will not be considered in the primary analysis (eg, if rotavirus is considered etiologic in addition to Shigella), but we will exclude episodes with multiple etiologies in a sensitivity analysis. Shigella will also be identified by bacterial culture in the main EFGH study, which will be considered in a separate sensitivity analysis.

Data Analysis

We will compare the diagnostic characteristics (eg, area under the curve, sensitivity, specificity) of the candidate biomarkers to identify watery diarrhea episodes that are attributed to Shigella, specific Shigella species, and other causes of bacterial diarrhea (eg, typical EPEC, ETEC, Campylobacter, Salmonella, and V cholerae) by qPCR. The correlation between markers will also be assessed to determine which may be complementary and/or redundant. Clinical prediction scores will be derived that incorporate the best-performing inflammatory biomarkers to identify an optimal diagnostic tool for watery bacterial diarrhea, and Shigella-attributed cases in particular. We will use SuperLearner, an algorithm that uses cross-validation to create an “ensemble” prediction model, which is an optimal weighted average of multiple machine learning models. Clinical and epidemiologic characteristics will include fever, duration of diarrhea, dehydration, vomiting, stool frequency, child age, season, length-for-age z score (LAZ, if <24 months of age) or height-for-age z score (HAZ, if ≥24 months of age), and breastfeeding at enrollment. Based on the variables included in the ensemble prediction model, we will create a more parsimonious prediction score based on scaled coefficients from a logistic regression model for bacterial etiology. This score could be practically applied in clinical settings; each characteristic included will be assigned points that would then be summed into a total score. Acknowledging the need to prioritize specificity to limit antibiotic overuse, we will derive cutoffs that maximize sensitivity at a minimum level of specificity of 80% to identify episodes that should be treated according to our algorithm. We will also report categories of confidence (eg, “most likely bacterial”) and/or percentage confidence based on the optimal machine learning algorithm. Finally, based on antibiotic treatments received and antibiotic susceptibility data [38], we will estimate the impact of appropriate antibiotic treatment on duration of diarrhea, hospitalization, 90-day mortality, and change in HAZ/LAZ in the subset of diarrhea episodes meeting the threshold for treatment based on the optimal treatment algorithm. We will compare the effects of treatment in the algorithm-defined subset with those among all episodes, in the subset of episodes that would be treated according to World Health Organization (WHO) guidelines (ie, dysentery only), and in etiology-specific subsets based on pathogen quantity detected by qPCR.

Preliminary analyses of MAL-ED data suggest that the biomarkers will be successful in identifying bacterial diarrhea. We added MPO concentrations that were de-trended for age to a clinical prediction score for Shigella previously developed in MAL-ED [7] to assess improvements in predictive ability of the score. The best improvements were achieved when MPO concentration was included with 5 categories and was weighed similarly heavily as child age. The clinical score alone achieved 40% sensitivity for identifying Shigella-attributable diarrhea episodes with 80% specificity (AUC = 0.74) in the subset of episodes that were also tested for MPO (n = 281). The addition of MPO increased sensitivity to 70% with 80% specificity (AUC = 0.79). This is a substantial improvement that may be more striking with the addition of multiple candidate biomarkers in a larger dataset.

POTENTIAL CHALLENGES AND LIMITATIONS

Our approach has some noteworthy limitations. Foremost, the algorithm will likely identify at least some episodes that should not be treated with antibiotics. Specifically, it could lead to unnecessary treatment of children with viral or parasitic diarrhea, which would facilitate antibiotic overuse and have implications for the development of antimicrobial resistance. However, use of even an imperfectly specific algorithm to inform treatment decisions would likely improve on current clinical practice given the often extreme overuse of antibiotics for diarrhea among children in low-resource settings [39]. The algorithm could also in theory cause harm by identifying bacterial diarrhea episodes for which antibiotics are contraindicated, for example, for children with Shiga toxin–producing E coli (STEC) [40]. However, the typical STEC clinical syndrome is bloody diarrhea without fever, whereas this algorithm would be primarily relevant for watery diarrhea since the WHO guidelines already recommend treatment for dysentery [6]. Furthermore, there was weak association between STEC and diarrhea in GEMS and MAL-ED, such that the role of STEC in children in resource-limited settings may be limited [7, 41]. Another key limitation is that the biomarker tests will not provide antibiotic susceptibility results, such that the algorithm will not be able to determine which antibiotic is likely to be effective in cases in which antibiotics are indicated. Clinicians will have to continue to rely on any available local susceptibility data.

There will also be several challenges in the research methods. Because not all participants will produce a stool specimen at the clinic during the enrollment visit, we will include stool samples produced within 24 hours of leaving the enrollment facility to achieve the sample size required to adequately power the study. This may confound the results in 2 ways. First, sampling will be performed at variable time points from the onset of illness, which may impact the levels of biomarkers within stool. Second, since antibiotics are likely to be administered before and/or at the enrollment visit, the biomarker levels in stools may have changed as a result of antibiotic action. We will adjust for the time between presentation to care and sample collection in the analysis to mitigate this concern. We will evaluate whether the performance of the algorithm differs by time since symptom onset and antibiotics received, since there will also be heterogeneity in these factors at the POC.

The inclusion of stool samples collected at participants’ homes may also result in variable time in which samples are outside of cold chain. While NGAL, calprotectin, and MPO are stable at room temperature [34–36], hemoglobin degrades up to 50% per day at room temperature [33]. In addition, to maximize the efficiency of sample testing, stool samples may be stored frozen for variable amounts of time and up to several months before being tested. While the test manufacturers stipulate that stool samples for the NGAL and calprotectin assays can be stored at −20°C for 1 year, samples for the hemoglobin and MPO assays should not be stored at −20°C for more than 1 and 2.5 months, respectively [33–36]. We will test all samples regardless and adjust for storage time in the analysis as necessary.

Next, the quantitative biomarker assays employed in this study require 15–50 mg of whole stool. The gold standard diagnostic comparator (pathogen detection by qPCR) will be evaluated in rectal swabs collected during the enrollment visit, rather than in the same whole stool sample in which the biomarkers will be tested. This aligns with the parent study protocol, which specifies using rectal swabs for qPCR to ensure etiology information is available for every enrolled case. Biomarker concentrations and/or pathogen detection may differ between the 2 samples due to differences in sample type and time of collection. While some patients and providers may prefer collection of whole stool rather than rectal swabs, whole stool may be an impractical clinical specimen on which to base a POC test for bacterial diarrhea since a rapid result would be required to guide clinical management. Should this study show that biomarkers are useful for predicting which children have bacterial diarrhea, future work will need to establish the validity of rectal swabs for POC biomarker testing.

A strength of our study is the inclusion of all medically attended diarrhea cases with etiology determined by qPCR among children from a diverse range of geographical locations. The inclusion of less-severe diarrhea is important since mortality is likely to be similar in both moderate-to-severe and less-severe cases [42]. The inclusion of 6 different study locations, however, presents a challenge when ensuring standardization of laboratory procedures between multiple sites. To ensure that the results are reproducible between sites, standardized ELISA training is being performed at all study sites and the centralized analysis platform will facilitate QC monitoring. In addition, there may be regional differences in the baseline levels of inflammatory biomarkers between populations driven by population genetics, the microbiota, subclinical infections, and/or differences in diet. If large enough, these differences may mean that generalizable biomarker concentration cutoff levels capable of guiding bacterial diagnosis cannot be established. We will evaluate and describe heterogeneity in the performance of the algorithm by study site.

DISCUSSION

Despite the limitations of the included studies in the systematic review, the sensitivity of all biomarkers to identify Shigella was high, and sensitivity to identify combined bacterial outcomes was moderate. Not surprisingly, specificity was lower for Shigella than for combined bacterial outcomes since multiple causes of diarrhea are inflammatory. The insensitivity of culture for Shigella may also have resulted in lower estimated specificities for Shigella. Performance was broadly comparable across biomarkers, justifying the assessment of multiple markers of leukocytes and erythrocytes in EFGH.

Our proposed inflammatory biomarker substudy will improve on prior efforts to use fecal biomarkers to identify bacterial diarrhea by applying ELISAs and molecular diagnostics in a large, geographically diverse study population. By combining inflammatory biomarker test results with clinical prediction scores to maximize predictive validity, we will determine whether POC biomarker tests would be a viable strategy to improve appropriate antibiotic treatment of watery bacterial diarrhea episodes. In the absence of readily available diagnostics for enteric pathogens, these tools could improve short- and long-term outcomes of diarrhea. If the inflammatory biomarkers are acceptably sensitive and specific, further development of low-cost POC biomarker tests would be warranted. Conversely, if these tools are not adequate, development of low-cost assays for the direct detection of enteropathogens in the clinical setting should be prioritized. Either type of POC test would have the dual benefit of increasing appropriate treatment of the episodes that are likely to respond, while also reducing inappropriate and overtreatment of viral and parasitic episodes. In settings where antibiotic overuse is common, the application of such tests may improve access to appropriate therapy while decreasing antibiotic use for diarrhea overall.

Supplementary Data

Supplementary materials are available at Open Forum Infectious Diseases online. Consisting of data provided by the authors to benefit the reader, the posted materials are not copyedited and are the sole responsibility of the authors, so questions or comments should be addressed to the corresponding author.

Supplementary Material

ofad652_Supplementary_Data

Contributor Information

Courtney Babb, Department of Epidemiology, Emory University, Atlanta, Georgia, USA.

Henry Badji, Medical Research Council Unit The Gambia, London School of Hygiene and Tropical Medicine, Fajara, The Gambia.

Md Taufiqur Rahman Bhuiyan, Infectious Diseases Division, International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh.

Jennifer Cornick, Institute of Infection, Veterinary and Ecological Sciences, Department of Clinical Infection, Microbiology and Immunology, University of Liverpool, Liverpool, United Kingdom; Malawi Liverpool Wellcome Programme, Blantyre, Malawi.

Sonia Qureshi, Department of Pediatrics and Child Health, The Aga Khan University, Karachi, Pakistan.

Catherine Sonye, Center for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya.

Wagner V Shapiama Lopez, Asociación Benéfica PRISMA, Iquitos, Peru.

Mehreen Adnan, Department of Pediatrics and Child Health, The Aga Khan University, Karachi, Pakistan.

Hannah E Atlas, Department of Global Health, University of Washington, Seattle, Washington, USA.

Kehkashan Begum, Department of Pediatrics and Child Health, The Aga Khan University, Karachi, Pakistan.

Stephanie A Brennhofer, Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, Virginia, USA.

Bubacarr E Ceesay, Medical Research Council Unit The Gambia, London School of Hygiene and Tropical Medicine, Fajara, The Gambia.

Abdoulie K Ceesay, Medical Research Council Unit The Gambia, London School of Hygiene and Tropical Medicine, Fajara, The Gambia.

Nigel A Cunliffe, Institute of Infection, Veterinary and Ecological Sciences, Department of Clinical Infection, Microbiology and Immunology, University of Liverpool, Liverpool, United Kingdom.

Paul F Garcia Bardales, Asociación Benéfica PRISMA, Iquitos, Peru.

Shahinur Haque, Infectious Diseases Division, International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh.

Bri’Anna Horne, Center for Vaccine Development and Global Health, University of Maryland School of Medicine, Baltimore, Maryland, USA; Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA.

M Jahangir Hossain, Medical Research Council Unit The Gambia, London School of Hygiene and Tropical Medicine, Fajara, The Gambia.

Junaid Iqbal, Department of Pediatrics and Child Health, The Aga Khan University, Karachi, Pakistan.

Md Taufiqul Islam, Infectious Diseases Division, International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh.

Sadia Islam, Infectious Diseases Division, International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh.

Farhana Khanam, Infectious Diseases Division, International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh.

Karen L Kotloff, Center for Vaccine Development and Global Health, University of Maryland School of Medicine, Baltimore, Maryland, USA; Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA.

Thandizo Malemia, Malawi Liverpool Wellcome Programme, Blantyre, Malawi.

Katia Manzanares Villanueva, Asociación Benéfica PRISMA, Iquitos, Peru.

Gertrude Malola Million, Malawi Liverpool Wellcome Programme, Blantyre, Malawi.

Vitumbiko Munthali, Malawi Liverpool Wellcome Programme, Blantyre, Malawi.

John Benjamin Ochieng, Center for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya.

Billy Ogwel, Center for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya.

Maribel Paredes Olortegui, Asociación Benéfica PRISMA, Iquitos, Peru.

Richard Omore, Center for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya.

Patricia B Pavlinac, Department of Global Health, University of Washington, Seattle, Washington, USA.

James A Platts-Mills, Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, Virginia, USA.

Khandra T Sears, Center for Vaccine Development and Global Health, University of Maryland School of Medicine, Baltimore, Maryland, USA; Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA.

Ousman Secka, Medical Research Council Unit The Gambia, London School of Hygiene and Tropical Medicine, Fajara, The Gambia.

Sharon M Tennant, Center for Vaccine Development and Global Health, University of Maryland School of Medicine, Baltimore, Maryland, USA; Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA.

Pablo Peñataro Yori, Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, Virginia, USA.

Mohammad Tahir Yousafzai, Department of Pediatrics and Child Health, The Aga Khan University, Karachi, Pakistan.

Khuzwayo C Jere, Institute of Infection, Veterinary and Ecological Sciences, Department of Clinical Infection, Microbiology and Immunology, University of Liverpool, Liverpool, United Kingdom; Malawi Liverpool Wellcome Programme, Blantyre, Malawi; Department of Medical Laboratory Sciences, Kamuzu University of Health Sciences, School of Life Sciences and Health Professions, Blantyre, Malawi.

Margaret N Kosek, Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, Virginia, USA.

Stephen Munga, Center for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya.

Usman N Ikumapayi, Medical Research Council Unit The Gambia, London School of Hygiene and Tropical Medicine, Fajara, The Gambia.

Firdausi Qadri, Infectious Diseases Division, International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh.

Farah Naz Qamar, Department of Pediatrics and Child Health, The Aga Khan University, Karachi, Pakistan.

Elizabeth T Rogawski McQuade, Department of Epidemiology, Emory University, Atlanta, Georgia, USA.

Notes

Author contributions. C. B., H. B., T. R. B., J. C., S. Q., C. S., W. V. S. L., K. C. J., M. N. K., S. M., U. N. I., F. N. Q., F. Q., and E. T. R. M. actively participated in monthly working group meetings during which the conceptualization and outline were discussed and agreed upon. C. B., H. B., W. V. S. L., S. Q., and E. T. R. M. conducted the systematic review. C. B., H. B., T. R. B., J. C., S. Q., C. S., W. V. S. L., and E. T. R. M. wrote the first draft of the manuscript. K. C. J., M. N. K., S. M., U. N. I., F. N. Q., and F. Q. provided review, scientific input, and editing of the first draft of the manuscript. M. A., H. E. A., K. B., S. A. B., B. E. C., A. K. C., N. A. C., P. F. G. B., S. H., B. H., M. J. H., J. I., T. I., S. I., F. K., K. L. K., T. M., K. M. V., G. M. M., V. M., J. B. O., B. O., M. P. O., R. O., P. B. P., J. A. P.-M., K. T. S., O. S., S. M. T., P. P. Y., and M. T. Y. reviewed and edited the manuscript. All authors approved the content of the final manuscript.

Disclaimer. The views expressed are those of the author(s) and not necessarily those of the National Institute for Health and Care Research (NIHR), the Department of Health and Social Care, the UK government, or the UK Health Security Agency.

Financial support. This project is supported by the Bill & Melinda Gates Foundation (grant numbers INV-016650, INV-031791, INV-036891, INV-036892, INV-028721, INV-041730, INV-044311, INV-044317) and the US National Institutes of Health (grant number D43TW010913 to M. N. K. and M. P. O.). The Gambia team's work is also supported by the UK Research and Innovation Medical Research Council (program number MC_UU_00031—Disease Control and Elimination). N. A. C. is an NIHR Senior Investigator (NIHR203756). N. A. C., J. C., and K. C. J. are affiliated with the NIHR Global Health Research Group on Gastrointestinal Infections at the University of Liverpool, and with the NIHR Health Protection Research Unit in Gastrointestinal Infections at the University of Liverpool, a partnership with the UK Health Security Agency in collaboration with the University of Warwick.

Supplement sponsorship . This article appears as part of the supplement “Enterics for Global Health (EFGH) Shigella Surveillance Study-Rationale and Methods,” sponsored by the Bill & Melinda Gates Foundation.

References

  • 1. Christopher  PR, David  KV, John  SM, Sankarapandian  V. Antibiotic therapy for Shigella dysentery. Cochrane Database Syst Rev  2010; 2010:CD006784. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2. Mendizábal-Morris  CA, Mata  LJ, Gangarosa  EJ, Guzmán  G. Epidemic Shiga-bacillus dysentery in Central America. Derivation of the epidemic and its progression in Guatemala, 1968-69. Am J Trop Med Hyg  1971; 20:927–33. [DOI] [PubMed] [Google Scholar]
  • 3. Pavlinac  PB, Platts-Mills  J, Liu  J, et al.  Azithromycin for bacterial watery diarrhea: a reanalysis of the AntiBiotics for Children with severe Diarrhea (ABCD) trial incorporating molecular diagnostics [manuscript published online ahead of print 5 July 2023]. J Infect Dis  2023. doi: 10.1093/infdis/jiad252 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4. Nasrin  D, Blackwelder  WC, Sommerfelt  H, et al.  Pathogens associated with linear growth faltering in children with diarrhea and impact of antibiotic treatment: the Global Enteric Multicenter Study. J Infect Dis  2021; 224:S848–55. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5. Buchwald  AG, Verani  JR, Keita  AM, et al.  Etiology, presentation, and risk factors for diarrheal syndromes in 3 sub-Saharan African countries after the introduction of rotavirus vaccines from the Vaccine Impact on Diarrhea in Africa (VIDA) study. Clin Infect Dis  2023; 76:S12–22. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6. World Health Organization . The treatment of diarrhoea: a manual for physicians and other senior health workers. 2005. Available at: https://www.who.int/publications/i/item/9241593180. Accessed 12 June 2023.
  • 7. Platts-Mills  JA, Liu  J, Rogawski  ET, et al.  Use of quantitative molecular diagnostic methods to assess the aetiology, burden, and clinical characteristics of diarrhoea in children in low-resource settings: a reanalysis of the MAL-ED cohort study. Lancet Glob Health  2018; 6:e1309–18. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8. Kasumba  IN, Badji  H, Powell  H, et al.  Shigella in Africa: new insights from the Vaccine Impact on Diarrhea in Africa (VIDA) study. Clin Infect Dis  2023; 76:S66–76. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9. Pavlinac  PB, Platts-Mills  JA, Tickell  KD, et al.  The clinical presentation of culture-positive and culture-negative, quantitative polymerase chain reaction (qPCR)–attributable shigellosis in the Global Enteric Multicenter Study and derivation of a Shigella severity score: implications for pediatric Shigella vaccine trials. Clin Infect Dis  2021; 73:e569–79. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10. McQuade  ETR, Shaheen  F, Kabir  F, et al.  Epidemiology of Shigella infections and diarrhea in the first two years of life using culture-independent diagnostics in 8 low-resource settings. PLoS Negl Trop Dis  2020; 14:e0008536. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11. Mattock  E, Blocker  AJ. How do the virulence factors of Shigella work together to cause disease?  Front Cell Infect Microbiol  2017; 7:64. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12. Kosek  M, Ahmed  T, Bhutta  Z, et al.  Causal pathways from enteropathogens to environmental enteropathy: findings from the MAL-ED birth cohort study. EBioMedicine  2017; 18:109–17. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13. Paccagnini  S, Fontana  M, Ceriani  R, et al.  Occult blood and faecal leucocyte tests in acute infectious diarrhoea in children. Lancet  1987; 329:442. [DOI] [PubMed] [Google Scholar]
  • 14. Korzeniowski  OM, Barada  FA, Rouse  JD, Guerrant  RL. Value of examination for fecal leukocytes in the early diagnosis of shigellosis. Am J Trop Med Hyg  1979; 28:1031–35. [DOI] [PubMed] [Google Scholar]
  • 15. Hossain  MA, Albert  MJ. Effect of duration of diarrhoea and predictive values of stool leucocytes and red blood cells in the isolation of different serogroups or serotypes of Shigella. Trans R Soc Trop Med Hyg  1991; 85:664–66. [DOI] [PubMed] [Google Scholar]
  • 16. Huicho  L, Sanchez  D, Contreras  M, et al.  Occult blood and fecal leukocytes as screening tests in childhood infectious diarrhea: an old problem revisited. Pediatr Infect Dis J  1993; 12:474–77. [DOI] [PubMed] [Google Scholar]
  • 17. Khan  AI, Huq  S, Malek  MA, et al.  Analysis of fecal leukocytes and erythrocytes in Shigella infections in urban Bangladesh. Southeast Asian J Trop Med Public Health  2006; 37:747–54. [PubMed] [Google Scholar]
  • 18. Mercado  EH, Ochoa  TJ, Ecker  L, et al.  Fecal leukocytes in children infected with diarrheagenic Escherichia coli. J Clin Microbiol  2011; 49:1376–81. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19. Lee  HM, Lee  S, Lee  B-I, et al.  Clinical significance of fecal lactoferrin and multiplex polymerase chain reaction in patients with acute diarrhea. Gut Liver  2015; 9:636–40. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20. Choi  SW, Park  CH, Silva  TM, Zaenker  EI, Guerrant  RL. To culture or not to culture: fecal lactoferrin screening for inflammatory bacterial diarrhea. J Clin Microbiol  1996; 34:928–32. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21. Huicho  L, Garaycochea  V, Uchima  N, Zerpa  R, Guerrant  RL. Fecal lactoferrin, fecal leukocytes and occult blood in the diagnostic approach to childhood invasive diarrhea. Pediatr Infect Dis J  1997; 16:644–47. [DOI] [PubMed] [Google Scholar]
  • 22. Ruiz-Peláez  JG, Mattar  S. Accuracy of fecal lactoferrin and other stool tests for diagnosis of invasive diarrhea at a Colombian pediatric hospital. Pediatr Infect Dis J  1999; 18:342–6. [DOI] [PubMed] [Google Scholar]
  • 23. Duman  M, Gencpinar  P, Biçmen  M, et al.  Fecal calprotectin: can be used to distinguish between bacterial and viral gastroenteritis in children?  Am J Emerg Med  2015; 33:1436–9. [DOI] [PubMed] [Google Scholar]
  • 24. Chen  C-C, Huang  J-L, Chang  C-J, Kong  M-S. Fecal calprotectin as a correlative marker in clinical severity of infectious diarrhea and usefulness in evaluating bacterial or viral pathogens in children. J Pediatr Gastroenterol Nutr  2012; 55:541–7. [DOI] [PubMed] [Google Scholar]
  • 25. Lepe-Balsalobre  E, Rubio-Sánchez  R, Úbeda  C, Úbeda Ontiveros  JM. Diagnostic utility of fecal calprotectin in chronic diarrhea of bacterial etiology in pediatric patients. Enferm Infecc Microbiol Clin (Engl Ed)  2021; 39:307–8. [DOI] [PubMed] [Google Scholar]
  • 26. Shastri  YM, Bergis  D, Povse  N, et al.  Prospective multicenter study evaluating fecal calprotectin in adult acute bacterial diarrhea. Am J Med  2008; 121:1099–106. [DOI] [PubMed] [Google Scholar]
  • 27. Sýkora  J, Siala  K, Huml  M, Varvařovská  J, Schwarz  J, Pomahačová  R. Evaluation of faecal calprotectin as a valuable non-invasive marker in distinguishing gut pathogens in young children with acute gastroenteritis. Acta Paediatr  2010; 99:1389–95. [DOI] [PubMed] [Google Scholar]
  • 28. Covidence . Systematic review tool. Available at: www.covidence.org. Accessed 12 June 2023.
  • 29. Whiting  P, Rutjes  AW, Reitsma  JB, Bossuyt  PM, Kleijnen  J. The development of QUADAS: a tool for the quality assessment of studies of diagnostic accuracy included in systematic reviews. BMC Med Res Methodol  2003; 3:25. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30. Ismail . Indicators for antibiotic therapy in invasive bacterial diarrhoea—record details—Embase. 1994. Available at: https://www.embase.com/records?subaction=viewrecord&rid=2&page=1&id=L25048116. Accessed 10 July 2023.
  • 31. Park  Y, Son  M, Jekarl  DW, Choi  HY, Kim  SY, Lee  S. Clinical significance of inflammatory biomarkers in acute pediatric diarrhea. Pediatr Gastroenterol Hepatol Nutr  2019; 22:369–76. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32. Atlas  HE, Conteh  B, Islam  MT, et al.  Diarrhea case surveillance in the Enterics for Global Health Shigella surveillance study: epidemiologic methods. Open Forum Infect Dis  2024; 11(Suppl 1):S6–16. [DOI] [PMC free article] [PubMed]
  • 33. Immundiagnostik  AG. IDK hemoglobin ELISA: manual. 2022. Available at: https://www.immundiagnostik.com/media/pages/testkits/k-7816d/8a2f137078-1679409720/k7816d_2022-02-22_haemoglobin.pdf. Accessed 14 July 2023.
  • 34. Immundiagnostik  AG. IDK calprotectin ELISA: manual. 2022. Available at: https://www.immundiagnostik.com/media/pages/testkits/k-6927/ef2d689aad-1679409720/k6927_2022-02-15_idk_calprotectin_stuhl_1h.pdf. Accessed 14 July 2023.
  • 35. Immundiagnostik  AG. IDK MPO ELISA: manual. 2022. Available at: https://www.immundiagnostik.com/media/pages/testkits/k-6630/10684925dd-1679409720/k6630_2022-01-26_mpo.pdf. Accessed 14 July 2023.
  • 36. Eagle Biosciences . NGAL (stool) ELISA assay kit: package insert. Available at: https://eaglebio.com/wp-content/uploads/2014/06/NGL35-K01_NGAL_Stool_ELISA_Assay_Kit_Package_Insert_v7.1_07.2023-1.pdf. Accessed 14 July 2023.
  • 37. Liu  J, Garcia Bardales  PF, Islam  K. Shigella detection and molecular serotyping with a customized TaqMan Array Card in the Enterics for Global Health (EFGH): Shigella surveillance study. Open Forum Infect Dis  2024; 11(Suppl 1):S34–40. [DOI] [PMC free article] [PubMed]
  • 38. Horne  B, Badji  H, Bhuiyan  TR, et al.  Microbiological methods used in the Enterics for Global Health Shigella surveillance study. Open Forum Infect Dis  2024; 11(Suppl 1):S25–33. [DOI] [PMC free article] [PubMed]
  • 39. Rogawski  ET, Platts-Mills  JA, Seidman  JC, et al.  Use of antibiotics in children younger than two years in eight countries: a prospective cohort study. Bull World Health Organ  2017; 95:49–61. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40. Freedman  SB, Xie  J, Neufeld  MS, et al.  Shiga toxin–producing Escherichia coli infection, antibiotics, and risk of developing hemolytic uremic syndrome: a meta-analysis. Clin Infect Dis  2016; 62:1251–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41. Liu  J, Platts-Mills  JA, Juma  J, et al.  Use of quantitative molecular diagnostic methods to identify causes of diarrhoea in children: a reanalysis of the GEMS case-control study. Lancet  2016; 388:1291–301. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42. Levine  MM, Nasrin  D, Acácio  S, et al.  Diarrhoeal disease and subsequent risk of death in infants and children residing in low-income and middle-income countries: analysis of the GEMS case-control study and 12-month GEMS-1A follow-on study. Lancet Glob Health  2020; 8:e204–14. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43. Bardhan  PK, Beltinger  J, Beltinger  RW, Hossain  A, Mahalanabis  D, Gyr  K. Screening of patients with acute infectious diarrhoea: evaluation of clinical features, faecal microscopy, and faecal occult blood testing. Scand J Gastroenterol  2000; 35:54–60. [DOI] [PubMed] [Google Scholar]
  • 44. Beltinger  J, Walther  R, Bardhan  P, Mahalanabis  D, Gyr  K. Immunological testing for occult blood in patients with acute infectious diarrhea (can it improve the specificity of the guaiac test?). Dig Dis Sci  1997; 42:366–71. [DOI] [PubMed] [Google Scholar]
  • 45. Bodhidatta  L, Vjthayasai  N, Eimpokalarp  B, Pitarangsi  C, Serichantalergs  O, Isenbarger  D. Bacterial enteric pathogens in children with acute dysentery in Thailand: increasing importance of quinolone-resistant Campylobacter. Southeast Asian J Trop Med Public Health  2002; 33:752–7. [PubMed] [Google Scholar]
  • 46. Chang  H, Zhang  L, Ge  Y, et al.  A hospital-based case-control study of diarrhea in children in Shanghai. Pediatr Infect Dis J  2017; 36:1057–63. [DOI] [PubMed] [Google Scholar]
  • 47. Jindal  N, Arora  S. Role of faecal leucocytes in the diagnostic evaluation of acute diarrhoea. Indian J Med Sci  1991; 45:261–64. [PubMed] [Google Scholar]
  • 48. McNeely  WS, Dupont  HL, Mathewson  JJ, Oberhelman  RA, Ericsson  CD. Occult blood versus fecal leukocytes in the diagnosis of bacterial diarrhea: a study of U.S. travelers to Mexico and Mexican children. Am J Trop Med Hyg  1996; 55:430–33. [DOI] [PubMed] [Google Scholar]
  • 49. Mshana  SE, Joloba  ML, Kakooza  A, Kaddu-Mulindwa  D. Role of microscopic examination of stool specimens in the diagnosis of Campylobacter infection from children with acute diarrhoea in Kampala, Uganda. Tanzan J Health Res  2010; 12:100–3. [DOI] [PubMed] [Google Scholar]
  • 50. Nordlander  E, Phuphaisan  S, Bodhidatta  L, Arthur  J, Echeverria  P. Microscopic examination of stools and a latex slide agglutination test for the rapid identification of bacterial enteric infections in Khmer children. Diagn Microbiol Infect Dis  1990; 13:273–6. [DOI] [PubMed] [Google Scholar]
  • 51. Patwari  AK, Deb  M, Dudeja  M, Jayasheela  M, Agarwal  A, Singh  P. Clinical and laboratory predictors of invasive diarrhoea in children less than five years old. J Diarrhoeal Dis Res  1993; 11:211–6. [PubMed] [Google Scholar]
  • 52. Sebodo  T, Soetarjo  T, Sadjimin  T, Soenarto  Y, Sanborn  WR. Study on the etiology of diarrhea. J Trop Pediatr Environ Child Health  1978; 24:107–9. [DOI] [PubMed] [Google Scholar]
  • 53. Venkataraman  S, Ramakrishna  BS, Kang  G, Rajan  DP, Mathan  VI. Faecal lactoferrin as a predictor of positive faecal culture in south Indian children with acute diarrhoea. Ann Trop Paediatr  2003; 23:9–13. [DOI] [PubMed] [Google Scholar]
  • 54. Pender  MA, Smith  T, Brintz  BJ, et al.  Weather variables as important clinical predictors of bacterial diarrhoea among international travellers. J Travel Med  2022; 29:taac012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55. Wang  Y, Zhang  TP, Xiao  HL, Qi  HY, Yin  CH. Formulation of an early warning infectivity score system for adult patients with acute bacterial diarrhea. Biomed Environ Sci  2014; 27:65–9. [DOI] [PubMed] [Google Scholar]
  • 56. Alvarado  T. Faecal leucocytes in patients with infectious diarrhoea. Trans R Soc Trop Med Hyg  1983; 77:316–20. [DOI] [PubMed] [Google Scholar]
  • 57. Alzaher  MZ, Almugahwi  AA, Almulla  AA, Almeer  HH, Alshammasi  MM, El-Badry  AA. Diagnostic yield of stool culture and probable predictive factors, a single-center experience. Acta Biomed  2022; 93:e2022302. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58. Ascher  DP, Ednsada-Corpus  R. Clinical and laboratory predictors of bacterial diarrhea in a tropical environment. Mil Med  1991; 156:74–6. [PubMed] [Google Scholar]
  • 59. Caprioli  A, Pezzella  C, Morelli  R, et al.  Enteropathogens associated with childhood diarrhea in Italy. Pediatr Infect Dis J  1996; 15:876–83. [DOI] [PubMed] [Google Scholar]
  • 60. Denno  DM, Stapp  JR, Boster  DR, et al.  Etiology of diarrhea in pediatric outpatient settings. Pediatr Infect Dis J  2005; 24:142–48. [DOI] [PubMed] [Google Scholar]
  • 61. DuBois  D, Binder  L, Nelson  B. Usefulness of the stool Wight's stain in the emergency department. J Emerg Med  1988; 6:483–6. [DOI] [PubMed] [Google Scholar]
  • 62. Fan  K, Morris  AJ, Reller  LB. Application of rejection criteria for stool cultures for bacterial enteric pathogens. J Clin Microbiol  1993; 31:2233–5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 63. Koplan  JP, Fineberg  HV, Ferraro  MJ, Rosenberg  ML. Value of stool cultures. Lancet  1980; 316:413–6. [DOI] [PubMed] [Google Scholar]
  • 64. McIver  CJ, Hansman  G, White  P, Doultree  JC, Catton  M, Rawlinson  WD. Diagnosis of enteric pathogens in children with gastroenteritis. Pathology  2001; 33:353–8. [PubMed] [Google Scholar]
  • 65. Bouckenooghe  AR, Dupont  HL, Jiang  ZD, et al.  Markers of enteric inflammation in enteroaggregative Escherichia coli diarrhea in travelers. Am J Trop Med Hyg  2000; 62:711–3. [DOI] [PubMed] [Google Scholar]
  • 66. Lai  C-C, Ji  D-D, Wu  F-T, et al.  Etiology and risk factors of acute gastroenteritis in a Taipei emergency department: clinical features for bacterial gastroenteritis. J Epidemiol  2016; 26:216–23. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 67. Lever  RA, Tapper  L, Skarbek  S, Chiodini  PL, Armstrong  M, Bailey  RL. Predictors of aetiology and outcomes of acute gastrointestinal illness in returning travellers: a retrospective cohort analysis. BMC Infect Dis  2021; 21:599. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 68. Loosli  J, Gyr  K, Stalder  H, et al.  Etiology of acute infectious diarrhea in a highly industrialized area of Switzerland. Gastroenterology  1985; 88:75–9. [DOI] [PubMed] [Google Scholar]
  • 69. Miller  JR, Barrett  LJ, Kotloff  K, Guerrant  RL. A rapid test for infectious and inflammatory enteritis. Arch Intern Med  1994; 154:2660–4. [DOI] [PubMed] [Google Scholar]
  • 70. Scerpella  EG, Okhuysen  PC, Mathewson  JJ, et al.  Evaluation of a new latex agglutination test for fecal lactoferrin in travelers’ diarrhea. J Travel Med  1994; 1:68–71. [DOI] [PubMed] [Google Scholar]
  • 71. Siegel  D, Cohen  P, Neighbor  M, et al.  Predictive value of stool examination in acute diarrhea. Arch Pathol Lab Med  1987; 111:715–8. [PubMed] [Google Scholar]
  • 72. Tribble  DR, Baqar  S, Pang  LW, et al.  Diagnostic approach to acute diarrheal illness in a military population on training exercises in Thailand, a region of Campylobacter hyperendemicity. J Clin Microbiol  2008; 46:1418–25. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 73. Ashraf  H, Beltinger  J, Alam  NH, et al.  Evaluation of faecal occult blood test and lactoferrin latex agglutination test in screening hospitalized patients for diagnosing inflammatory and non-inflammatory diarrhoea in Dhaka, Bangladesh. Gastroenterologia  2007; 76:256–61. [DOI] [PubMed] [Google Scholar]
  • 74. Bardhan  PK, Beltinger  J, Beltinger  RW, Hossain  A, Mahalanabis  D, Gyr  K. Screening of patients with acute infectious diarrhoea: evaluation of clinical features, faecal microscopy, and faecal occult blood testing. Scand J Gastroenterol  2000; 35:54–60. [DOI] [PubMed] [Google Scholar]
  • 75. Aly  SM, El-Zawawy  LA, Said  DE, Fathy  FM, Mohamed  ON. The utility of lactoferrin in differentiating parasitic from bacterial infections. J Egypt Soc Parasitol  2005; 35:1149–62. [PubMed] [Google Scholar]
  • 76. Ahn  JS, Seo  SI, Kim  J, et al.  Efficacy of stool multiplex polymerase chain reaction assay in adult patients with acute infectious diarrhea. World J Clin Cases  2020; 8:3708–17. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 77. Berger  C, Loitsch  SM, Hartmann  F, Stein  J. 657 comparative evaluation of fecal calprotectin and S100A12 as non-invasive markers in predicting microbiological diagnosis for acute bacterial diarrhea: prospective multicenter study. Gastroenterology  2010; 5:S88. [Google Scholar]
  • 78. Czub  E, Nowak  JK, Moczko  J, et al.  Comparison of fecal pyruvate kinase isoform M2 and calprotectin in acute diarrhea in hospitalized children. Sci Rep  2014; 4:4769. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 79. Kim  HJ. Efficacy of fecal calprotectin combined with stool hemoglobin in differentiating bacterial origin in acute gastroenteritis. Pediatr Emerg Care  2022; 38:e670–3. [DOI] [PubMed] [Google Scholar]
  • 80. Aratani  Y. Myeloperoxidase: its role for host defense, inflammation, and neutrophil function. Arch Biochem Biophys  2018; 640:47–52. [DOI] [PubMed] [Google Scholar]
  • 81. Khan  AA, Alsahli  MA, Rahmani  AH. Myeloperoxidase as an active disease biomarker: recent biochemical and pathological perspectives. Med Sci (Basel)  2018; 6:33. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 82. Hansberry  DR, Shah  K, Agarwal  P, et al.  Fecal myeloperoxidase as a biomarker for inflammatory bowel disease. Cureus  2017; 9:e1004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 83. Guerrant  RL, Bolick  DT, Swann  JR. Modeling enteropathy or diarrhea with the top bacterial and protozoal pathogens: differential determinants of outcomes. ACS Infect Dis  2021; 7:1020–31. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 84. Sharbatdaran  M, Holaku  A, Kashifard  M, et al.  Fecal calprotectin level in patients with IBD and noninflammatory disease of colon: a study in Babol, northern Iran. Caspian J Intern Med  2018; 9:60–4. [PMC free article] [PubMed] [Google Scholar]
  • 85. Shastri  YM, Bergis  D, Povse  N, et al.  Prospective multicenter study evaluating fecal calprotectin in adult acute bacterial diarrhea. Am J Med  2008; 121:1099–106. [DOI] [PubMed] [Google Scholar]
  • 86. Jaberi  SA, Cohen  A, D'Souza  C, et al.  Lipocalin-2: structure, function, distribution and role in metabolic disorders. Biomed Pharmacother  2021; 142:112002. [DOI] [PubMed] [Google Scholar]
  • 87. Moschen  AR, Adolph  TE, Gerner  RR, Wieser  V, Tilg  H. Lipocalin-2: a master mediator of intestinal and metabolic inflammation. Trends Endocrinol Metab  2017; 28:388–97. [DOI] [PubMed] [Google Scholar]
  • 88. Li  D, Yan Sun  W, Fu  B, Xu  A, Wang  Y. Lipocalin-2—the myth of its expression and function. Basic Clin Pharmacol Toxicol  2020; 127:142–51. [DOI] [PubMed] [Google Scholar]
  • 89. Q S Medeiros  PH, Ledwaba  SE, Bolick  DT, et al.  A murine model of diarrhea, growth impairment and metabolic disturbances with Shigella flexneri infection and the role of zinc deficiency. Gut Microbes  2019; 10:615–30. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 90. Chassaing  B, Srinivasan  G, Delgado  MA, Young  AN, Gewirtz  AT, Vijay-Kumar  M. Fecal lipocalin 2, a sensitive and broadly dynamic non-invasive biomarker for intestinal inflammation. PLoS One  2012; 7:e44328. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 91. Walker  HK, Hall  WD, Hurst  JW, eds. Clinical methods: the history, physical, and laboratory examinations. 3rd ed. Boston, MA: Butterworths; 1990. [PubMed] [Google Scholar]
  • 92. Mooiweer  E, Fidder  HH, Siersema  PD, Laheij  RJF, Oldenburg  B. Fecal hemoglobin and calprotectin are equally effective in identifying patients with inflammatory bowel disease with active endoscopic inflammation. Inflamm Bowel Dis  2014; 20:307–14. [DOI] [PubMed] [Google Scholar]

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