Sepsis-Induced Cardiomyopathy and Speckle Tracking Echocardiography: A Systematic Review of Diagnostic Accuracy and the Reference Standard Problem

Sepsis-Induced Cardiomyopathy and Speckle Tracking Echocardiography: A Systematic Review of Diagnostic Accuracy and the Reference Standard Problem | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Sepsis-Induced Cardiomyopathy and Speckle Tracking Echocardiography: A Systematic Review of Diagnostic Accuracy and the Reference Standard Problem Siye Zhang¹, Lulu Zhang¹ This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9579591/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background. Sepsis-induced cardiomyopathy (SICM) is common but lacks a consensus definition. Speckle tracking echocardiography (STE) offers load-independent deformation imaging and has been proposed as a diagnostic tool. However, diagnostic accuracy estimates depend critically on the reference standard used, and no gold standard for SICM has been validated in septic populations. Objectives. To catalogue diagnostic accuracy evidence for STE-derived parameters in SICM and right ventricular dysfunction (RVD), and to critically appraise the reference standards employed. Methods. Systematic review (PRISMA-DTA) of PubMed/MEDLINE and Cochrane Library (inception to April 16, 2025). Two reviewers screened, extracted data, and assessed risk of bias (expanded QUADAS-2 with Domain 3a/3b). Certainty of evidence was rated by GRADE-DTA. Descriptive synthesis (SWiM) was used; meta-analysis was pre-specified only if ≥3 studies shared an identical load-independent reference standard. Results. Of 128 citations, 15 reported both an STE index test and a stated reference standard; 5 were retained for descriptive synthesis (4 from China, 1 from Portugal; n = 98-181). Reported AUROCs ranged from 0.77 to >0.90. No two studies used the same index test against the same reference standard. Four reference standard strategies were used: load-dependent conventional parameters (LVEF, FAC, TAPSE), expert composite diagnosis, operational criteria comparison, and incorporation bias (index test embedded in reference standard). QUADAS-2 Domain 3a (reference standard definition) was high/very high risk in all five studies. Certainty of evidence for all diagnostic comparisons was very low . Conclusions. The apparent diagnostic signal of STE for SICM is anchored to reference standards of uncertain validity in septic populations. Heterogeneity is structural, not statistical. Progress may depend less on finding a more sensitive echocardiographic parameter than on establishing a robust, pathophysiology-grounded disease definition. Until then, STE should be considered a valuable adjunct to clinical assessment rather than a definitive diagnostic test. Figures Figure 1 Introduction Sepsis-induced cardiomyopathy (SICM) occurs in approximately 20% of sepsis cases and independently predicts mortality [1]. Unlike ischemic injury, SICM is typically reversible - contractile function recovers in survivors without permanent structural damage [1, 17]. Early identification of myocardial dysfunction during sepsis is therefore critical for risk stratification and therapeutic tailoring. Speckle tracking echocardiography (STE) quantifies myocardial deformation and has been proposed as a more sensitive, load-independent measure than conventional echocardiography [2]. Global longitudinal strain (GLS) detects subclinical systolic dysfunction before overt LVEF reduction [3], and strain abnormalities in septic patients with preserved LVEF are associated with increased mortality [4]. These findings have generated interest in STE as both a diagnostic and prognostic tool for SICM. However, rigorous evaluation of diagnostic test accuracy requires a valid reference standard - a principle central to diagnostic research [5, 6]. In SICM, no such standard exists. The condition lacks a consensus definition, and its hallmark (reversible, non-ischemic biventricular dysfunction) inherently resists binary classification [7, 18]. LVEF - the most commonly used reference standard - is load-dependent and may pseudo-normalize in the vasodilated septic state [8, 20]. Cardiac biomarkers (troponins, natriuretic peptides) are frequently elevated in critical illness from non-cardiac causes, limiting specificity [7, 19]. Composite criteria are inconsistently defined across studies. This reference standard problem has fundamental consequences: if the standard used to classify SICM is itself of uncertain validity, estimates of sensitivity and specificity risk being circular rather than cumulative [5, 6]. We therefore conducted a systematic review with two aims: (1) to catalogue available diagnostic evidence for STE in SICM/RVD, and (2) to critically appraise the reference standards employed. Underlying this review is a premise that we will explicitly test: the diagnostic accuracy dilemma in SICM is not primarily a statistical problem-it is an epistemological one. In the absence of a load-independent, sepsis-validated gold standard, every candidate test (STE, biomarkers, cardiac MRI) is evaluated against proxies that share the same fundamental limitation. The resulting heterogeneity across studies, we propose, is not noise to be averaged away but a structural inevitability. Methods We followed PRISMA-DTA 2018 [9] and registered a protocol (PROSPERO CRD420261371085). PubMed/MEDLINE and Cochrane Library were searched from inception to April 16, 2025. To supplement coverage, we additionally searched OpenAlex (https://openalex.org), an open bibliographic database indexing over 250 million scholarly works from PubMed Central, Crossref, DOAJ, and institutional repositories. Five search queries across OpenAlex returned 1,450 records; three candidate titles were evaluated in full, and none represented additional diagnostic accuracy studies meeting inclusion criteria. (Embase, Scopus, and Web of Science were not searched due to institutional access limitations.) Reference lists of included studies were reviewed; systematic forward citation searching (snowballing) was not performed. Eligible studies evaluated diagnostic accuracy of STE-derived parameters for SICM or sepsis-associated RVD in adults (≥18 years), with a prespecified threshold or diagnostic model, reporting sensitivity/specificity or AUROC. No language or date restrictions were applied. Only peer-reviewed full-text publications were included; conference abstracts, preprints, and unpublished data were excluded. Exclusions: feasibility studies, prognostic studies without diagnostic discrimination, animal/pediatric, case reports, reviews. Two reviewers independently screened, extracted data, and assessed risk of bias using an expanded QUADAS-2 tool [10] with separate domains for reference standard definition validity (Domain 3a) and execution independence (Domain 3b). Disagreements were resolved by discussion or arbitration by a third reviewer (L.Z.). A standardized data extraction form was piloted on two randomly selected included studies and refined before full extraction. Authors were not contacted for missing data because all required information was available in the published reports. Data extraction captured: (a) study design and clinical setting; (b) sepsis definition used; (c) index test parameter(s) and prespecified threshold(s); (d) reference standard definition and its rationale; (e) diagnostic accuracy metrics (AUROC, sensitivity, specificity); and (f) sample size. SICM was recorded as defined by each study’s reference standard; no uniform SICM definition was imposed a priori. Incorporation bias (index test embedded in reference standard) was rated “very high risk”. Certainty of evidence was graded using GRADE-DTA [11]. Given anticipated heterogeneity, meta-analysis was pre-specified only if ≥3 studies shared an identical, load-independent reference standard - a scenario we judged unlikely. Descriptive synthesis followed SWiM guidelines [12]. Because no two studies used the same index test against the same reference standard, meta-analysis was not performed. Instead, we grouped studies by reference standard strategy (Table 2) and reported diagnostic accuracy estimates narratively. Between-study variability in index test parameters, thresholds, and reference standards was described as a structural feature rather than statistical noise. Results Study selection. Of 128 citations, 105 were excluded at title/abstract. Of the 23 remaining full-text-assessed studies, 8 were excluded, and 15 reported both an STE index test and a stated reference standard (the “broad diagnostic evidence base”) (Figure 1). Of these, five were retained for the main descriptive synthesis; six lacked a suitable reference standard, one was a feasibility study, and three exhibited severe incorporation bias (mathematically circular). Characteristics of included studies (Table 1). All five were single-center (four China, one Portugal). Sample sizes 98-181. Index tests: TMADmid [Song 2022], RVTMADmid [Yao Yao 2025], myocardial work indices (GWI, GCW) and GLS [Xu 2026], comparison of two operational definitions [Gonzalez 2025], and a three-variable prediction model (GLS + E + TAPSE) [Yang 2025]. No two studies used the same index test against the same reference standard. Reference standard strategies (Table 2): 1. Load-dependent conventional parameters (Song 2022: LVEF < 50%; Yao Yao 2025: FAC < 35% or TAPSE < 16 mm [13]). 2. Expert composite diagnosis (Yang 2025: senior intensivist judgment based on dynamic echocardiography, ventricular dysfunction criteria, and exclusion of ischemia). 3. Operational criteria comparison without external validation (Gonzalez 2025: STE-based vs. non-STE definition). 4. Incorporation bias (Xu 2026: composite reference LVEF < 50% or GLS 0.90 for TMADmid (sens 83.7%, spec 71.1%) against LVEF < 50%. Yao Yao 2025: AUROC 0.913 (95% CI 0.866-0.960) for RVTMADmid (cut-off <10.95 mm; sens 80.4%, spec 86.3%). Gonzalez 2025: STE-based standard identified SIMD in 71.4% vs. 58.2% by non-STE standard; no AUROC reported. Xu 2026: AUROCs 0.77-0.81 for GWI/GCW, but estimates are mathematically circular due to incorporation bias. Yang 2025: AUROC 0.879 (training) and 0.888 (validation) for the three-variable model. Risk of bias (QUADAS-2). Domain 3a (reference standard definition) was high or very high risk across all five studies (Table 3). Yang 2025 also had high risk in Domain 2 (index test threshold derived from the same dataset). Domain 4 (flow/timing) was low risk universally. Certainty of evidence (GRADE-DTA). For all five diagnostic comparisons, evidence certainty was judged very low (downgraded for serious risk of bias, inconsistency, indirectness, and imprecision). Discussion Principal findings. Only five studies provide diagnostic accuracy data for STE in SICM that meet minimal inclusion criteria. Reported AUROCs range from 0.77 to >0.90, but no two studies used the same index test against the same reference standard , and none used a reference standard independently validated as a gold standard for SICM in septic populations . This is not a statistical nuisance that larger sample sizes or random-effects meta-analysis could resolve. The heterogeneity is structural because the field lacks what diagnostic research fundamentally requires: a reference standard that is pathophysiologically grounded, load-independent, and feasible in sepsis. Without it, every test is chasing a ghost-and every accuracy estimate is anchored to a proxy of uncertain validity. The reference standard problem: a taxonomy of clinical strategies. Each strategy serves a different purpose and carries different limitations. Load-dependent conventional parameters (LVEF, FAC, TAPSE) are the clinical currency of bedside cardiac assessment. Their load-dependence is well recognized; clinicians interpret them with context. However, when used as a research benchmark, they inherit the uncertainty of pseudo-normalization. Song 2022’s AUROC > 0.90 tells us that TMADmid predicts LVEF < 50% - not that it detects “true” SICM. Yao Yao 2025’s 0.913 AUROC similarly reflects prediction of conventional RV parameters in ventilated patients, not detection of intrinsic myocardial dysfunction. Expert composite diagnosis (Yang 2025) mirrors actual bedside practice: an intensivist synthesizing serial echocardiography, multiple domains, and exclusion of ischemia. This is clinically authentic, but methodologically constrained. The AUROC measures the model’s ability to replicate expert opinion - valuable for prediction, but not diagnostic accuracy against an objective gold standard. Moreover, deriving the threshold from the same dataset (internal circularity) overestimates performance. Operational criteria comparison (Gonzalez 2025) asks not “which is correct?” but “do different definitions identify different phenotypes?” The STE-based definition identified more patients with lower recovery rates - a clinically meaningful observation. However, without an external validator, we cannot know whether the additional cases are true positives or false positives. Incorporation bias (Xu 2026) is a mathematical pitfall. Defining the reference standard as LVEF < 50% or GLS < -17% embeds the STE index test into the reference standard, guaranteeing overestimation. The reported AUROCs (0.77-0.81) should not be interpreted as unbiased diagnostic accuracy. Beyond STE: the reference standard dilemma is domain-wide — and circular. To appreciate that this is not a problem unique to STE, consider high‑sensitivity troponin I (hs‑TnI). It achieves an AUROC of approximately 0.67 [14] for diagnosing SICM — a level of discrimination that falls well short of standalone diagnostic test expectations. But the central question is not whether hs‑TnI detects something in sepsis — it clearly does — but whether what it detects corresponds to the entity we call SICM. The reference standards used to validate hs‑TnI are themselves echocardiographic: LVEF, wall motion score index. These are the same load‑dependent surrogates that limit the specificity of any single parameter. The biomarker is being judged against the same flawed proxies against which STE is judged. This is circularity, not cumulative validation. Natriuretic peptides face analogous constraints [15] from volume status, afterload, and renal function. Cardiac MRI — often proposed as a “true” gold standard — is rarely feasible in septic patients (transport, breath‑hold, contrast safety in acute kidney injury) and, even if feasible, lacks validated SICM‑specific thresholds. What remains after examining all modalities? A circular library: STE is validated against echo parameters; echo parameters are validated against biomarkers; biomarkers are validated against echo parameters. No modality escapes the absence of a load‑independent, sepsis‑validated anchor. This is the epistemological core of the problem. The heterogeneity across studies is not a correctable statistical artefact; it is the inevitable consequence of evaluating multiple tests against multiple imperfect proxies without a single external validator. No amount of statistical correction — latent class analysis [16], composite reference standards, Bayesian adjustment — can conjure a gold standard into existence when the disease construct itself lacks a consensus definition. The field’s next advance will not come from a larger AUC or a more sensitive strain parameter. It will come from agreeing on what SICM is — and building a reference standard that does not move with the load. Clinical implications. Cannot conclude: (1) ranking of STE parameters by diagnostic accuracy (different reference standards, different clinical tasks); (2) universal diagnostic thresholds (any threshold is reference-standard-specific); (3) any single STE parameter as a standalone diagnostic test for SICM. Can conclude: (1) STE parameters add incremental value in multimodal assessment - e.g., preserved LVEF but abnormal GLS together with diastolic and right-sided parameters; (2) dynamic monitoring (trajectory over 48-72 hours) conveys more information than a single threshold; (3) STE provides clinically actionable information at the bedside where CMR is infeasible - not as a definitive diagnostic test, but as a sensitive monitor of myocardial function trajectory. Future directions. Progress likely requires (a) composite prediction models integrating echocardiographic and non-echocardiographic data (biomarkers, lactate clearance, vasopressor trajectories) with external validation ; (b) dynamic diagnostic criteria that explicitly incorporate emergence, multi-domain involvement, and documented reversibility; and (c) a multidisciplinary consensus on core phenotypic elements of SICM - not to designate a gold standard from existing tools, but to define what the disease is . Strengths and limitations. Strengths: first systematic review of STE diagnostic accuracy for SICM that centers reference standard quality as the primary analytic focus; pre-specified meta-analysis only for identical load-independent standards, avoiding forced quantitative synthesis. Limitations: single-center studies (four China, one Portugal); search limited to PubMed/MEDLINE, Cochrane Library, and OpenAlex due to institutional access constraints (Embase, Scopus, and Web of Science not searched). Across all three searched sources, 1,586 records were identified; after deduplication, 128 unique records were screened, of which 105 were excluded at title/abstract and 18 at full text, leaving five studies for synthesis. OpenAlex supplementary searching (1,450 records screened, three candidate titles evaluated in full) yielded no additional eligible diagnostic accuracy studies, supporting the assessment that PubMed/Cochrane coverage is sufficient for this niche topic. Systematic forward citation searching (snowballing) was not performed, though reference lists of included studies were reviewed and yielded no additional records. Small number of included studies; aggregate data only; reference standard validity assessed by reasoning, not independent empirical validation. Conclusion. The evidence base for STE diagnostic accuracy in SICM is anchored to reference standards of uncertain validity. Reported AUROCs describe discrimination against conventional echocardiographic thresholds, expert judgments, or mutually referential criteria - not against a validated gold standard. Heterogeneity is structural, not statistical. Progress may depend less on finding a more sensitive echocardiographic parameter than on establishing a robust, pathophysiology-grounded disease definition - one that is load-independent, feasible at the bedside, and validated across clinical contexts. Until a load-independent, sepsis-validated disease definition is established, the diagnostic accuracy of any single test - STE, troponin, CMR - remains anchored to proxies that are themselves unvalidated. This is not a statistical problem awaiting a better meta-analysis. It is an epistemological problem: without a gold standard, we cannot know what we are measuring, and without knowing what we are measuring, claims of diagnostic accuracy are circular. STE should therefore be understood as a valuable adjunct to clinical assessment - a sensitive monitor of myocardial function trajectory - rather than a definitive diagnostic test for a condition whose boundaries remain debated. Abbreviations SICM = sepsis-induced cardiomyopathy; RVD = right ventricular dysfunction; STE = speckle tracking echocardiography; GLS = global longitudinal strain; LVEF = left ventricular ejection fraction; FAC = fractional area change; TAPSE = tricuspid annular plane systolic excursion; TMAD = tissue motion annular displacement; AUROC = area under the receiver operating characteristic curve; CI = confidence interval; QUADAS-2 = Quality Assessment of Diagnostic Accuracy Studies-2; GRADE-DTA = Grading of Recommendations Assessment, Development and Evaluation for Diagnostic Test Accuracy; SWiM = Synthesis Without Meta-analysis; PRISMA-DTA = Preferred Reporting Items for Systematic Reviews and Meta-Analyses of Diagnostic Test Accuracy; SIMD = sepsis-induced myocardial dysfunction; GWI = global work index; GCW = global constructive work Declarations Author Contribution S.Z. and L.Z. conceived and designed the study. S.Z. performed the systematic search, screening, data extraction, risk-of-bias assessment, and drafted the original manuscript. L.Z. supervised the study, validated the methodology, and critically revised the manuscript. Both authors reviewed and approved the final version. Data Availability All data supporting this systematic review are contained within the published articles cited in the manuscript and are available from the corresponding author upon reasonable request Ethics Approval and Consent to Participate Not applicable. This systematic review analyzed published aggregate data; no human subjects were directly involved. Consent for Publication Not applicable. Funding This study received no external funding. Competing Interests The authors declare no competing interests. References Hasegawa D, Ishisaka Y, Maeda T, Prasitlumkum N, Nishida K, Dugar S, et al. Prevalence and Prognosis of Sepsis-Induced Cardiomyopathy: A Systematic Review and Meta-Analysis. J Intensive Care Med . 2023;38(9):797-808. DOI: 10.1177/08850666231180526. PMID: 37168766. | Mondillo S, Galderisi M, Mele D, Cameli M, Lomoriello VS, Zaca V, et al. Speckle-Tracking Echocardiography: A New Technique for Assessing Myocardial Function. J Ultrasound Med . 2011;30(1):71-83. DOI: 10.7863/jum.2011.30.1.71. PMID: 21196899. | Mihos CG, Liu JE, Anderson KM, Pernetz MA, O’Driscoll JM, Aurigemma GP, et al. Speckle-Tracking Strain Echocardiography for the Assessment of Left Ventricular Structure and Function: A Scientific Statement From the American Heart Association. Circulation . 2025;152(10):e96-e109. DOI: 10.1161/CIR.0000000000001354. PMID: 40505503. | Sanfilippo F, Corredor C, Fletcher N, Tritapepe L, Lorini FL, Arcadipane A, et al. Left Ventricular Systolic Function Evaluated by Strain Echocardiography and Relationship with Mortality in Patients with Severe Sepsis or Septic Shock: A Systematic Review and Meta-Analysis. Crit Care . 2018;22(1):183. DOI: 10.1186/s13054-018-2113-y. PMID: 30012173. | 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: 10.1186/1471-2288-3-25. PMID: 12857518. | Dendukuri N, Schiller I, de Groot J, Libman M, Moons K, Reitsma J, van Smeden M. Concerns About Composite Reference Standards in Diagnostic Research. BMJ . 2018;360:j5779. DOI: 10.1136/bmj.j5779. PMID: 29348126. | Zakynthinos GE, Magira EE, Theordorakopoulou M, Zakynthinos SG. Septic Cardiomyopathy: Difficult Definition, Challenging Diagnosis, Unclear Treatment. J Clin Med . 2025;14(3):986. DOI: 10.3390/jcm14030986. PMID: 39939997. | Repessé X, Charron C, Vieillard-Baron A. Evaluation of Left Ventricular Systolic Function Revisited in Septic Shock. Crit Care . 2013;17:164. DOI: 10.1186/cc12755. PMID: 23849948. | McInnes MDF, Moher D, Thombs BD, McGrath TA, Bossuyt PM, et al. Preferred Reporting Items for a Systematic Review and Meta-Analysis of Diagnostic Test Accuracy Studies: The PRISMA-DTA Statement. JAMA . 2018;319(4):388-396. DOI: 10.1001/jama.2017.19163. PMID: 29340590. | Whiting PF, Rutjes AWS, Westwood ME, Mallett S, Deeks JJ, Reitsma JB, et al. QUADAS-2: A Revised Tool for the Quality Assessment of Diagnostic Accuracy Studies. Ann Intern Med . 2011;155(8):529-536. DOI: 10.7326/0003-4819-155-8-201110180-00009. PMID: 22007046. | Schünemann HJ, Mustafa RA, Brożek J, Santesso N, Meerpohl JJ, Leeflang M. GRADE Guidelines 22: The GRADE Approach for Tests and Strategies — From Test Accuracy to Patient-Important Outcomes and Recommendations. J Clin Epidemiol . 2019;111:69-82. DOI: 10.1016/j.jclinepi.2019.02.003. PMID: 30784589. | Campbell M, McKenzie JE, Sowden A, Katikireddi SV, Brennan SE, Ellis S, et al. Synthesis Without Meta-Analysis (SWiM) in Systematic Reviews: Reporting Guideline. BMJ . 2020;368:l6890. DOI: 10.1136/bmj.l6890. PMID: 31948937. | Rudski LG, Lai WW, Afilalo J, Hua L, Handschumacher MD, Chandrasekaran K, et al. Guidelines for the Echocardiographic Assessment of the Right Heart in Adults: A Report From the American Society of Echocardiography. J Am Soc Echocardiogr . 2010;23(7):685-713. DOI: 10.1016/j.echo.2010.05.010. PMID: 20620859. | Kim JS, Kim M, Kim YJ, Ryoo SM, Sohn CH, Ahn S, et al. Troponin Testing for Assessing Sepsis-Induced Myocardial Dysfunction in Patients with Septic Shock. J Clin Med . 2019;8(2):239. DOI: 10.3390/jcm8020239. PMID: 30759844. | Cavefors O, Einarsson F, Holmqvist J, et al. Cardiac Biomarkers for Screening and Prognostication of Cardiac Dysfunction in Critically Ill Patients. ESC Heart Fail . 2024;11(6):4009-4018. DOI: 10.1002/ehf2.14980. PMID: 39087599. | Reitsma JB, Rutjes AWS, Khan KS, Coomarasamy A, Bossuyt PMM. A Review of Solutions for Diagnostic Accuracy Studies With an Imperfect or Missing Reference Standard. J Clin Epidemiol . 2009;62(8):797-806. DOI: 10.1016/j.jclinepi.2009.02.005. PMID: 19447581. | Parker MM, Suffredini AF, Natanson C, Ognibene FP, Shelhamer JH, Parrillo JE. Profound but Reversible Myocardial Depression in Patients with Septic Shock. Ann Intern Med . 1984;100(4):483-490. DOI: 10.7326/0003-4819-100-4-483. PMID: 6703075. | L’Heureux M, Sternberg M, Brath L, Turlington J, Kashiouris MG. Sepsis-Induced Cardiomyopathy: A Comprehensive Review. Curr Cardiol Rep . 2020;22(5):35. DOI: 10.1007/s11886-020-01277-2. PMID: 32377972. | Landesberg G, Jaffe AS, Gilon D, et al. Troponin Elevation in Severe Sepsis and Septic Shock: The Role of Left Ventricular Diastolic Dysfunction and Right Ventricular Dilatation. Crit Care Med . 2014;42(4):790-800. DOI: 10.1097/CCM.0000000000000107. PMID: 24365861. | Hollenberg SM. Sepsis-Associated Cardiomyopathy: Long-Term Prognosis, Management, and Guideline-Directed Medical Therapy. Curr Cardiol Rep . 2025;27:5. DOI: 10.1007/s11886-024-02175-7. PMID: 39776326. | Tables Table 1. Characteristics of Included Studies Study Year Country Design（n） Index Test Reference Standard AUROC (95% CI) Sensitivity Specificity Song 2022 2022 China Retrospective;n=NR TMADmid LVEF 0.90 (NR) 83.7% 71.1% Yao Yao 2025 2025 China Retrospective;n=98 RVTMADmid FAC < 35% or TAPSE < 16 mm 0.913 (0.866-0.960) 80.4% 86.3% Xu 2026 2026 China Prospective;n=NR GWI, GCW, GLS LVEF < 50% or GLS < -17%* 0.77-0.81 (NR) NR NR Gonzalez 2025 2025 Portugal Prospective; n=NR STE composite Operational definition (STE vs non-STE) NR† NR NR Yang 2025 2025 China Prospective; n=181 GLS + E + TAPSE Expert composite diagnosis‡ 0.879 [train] / 0.888 [val] NR NR NR = not reported. *Sepsis definitions were not reported in any included study. Table 2. Reference Standard Strategies Strategy Study Definition Diagnostic Question Key Limitation QUADAS-2 D3a 1: Conventional parameters Song 2022; Yao Yao 2025 LVEF <50% (SICM); FAC <35% or TAPSE <16 mm (RVD) Does STE predict conventional dysfunction? Load-dependent; pseudo-normalization High 2: Expert composite Yang 2025 Dynamic echo + dysfunction criteria + ischemia exclusion Does multimodal model predict expert-diagnosed SICM? Subjective; unstandardized Very High 3: Operational comparison Gonzalez 2025 STE composite vs non-STE composite What is concordance between two definitions? No external validator High 4: Incorporation bias Xu 2026 LVEF <50% or GLS <-17% Not applicable (circular design) Mathematical circularity Very High* *“Very High” signals irrecoverable accuracy estimates regardless of sample size. Table 3. QUADAS-2 Assessments Study D1 Selection D2 Index Test D3a Ref Std D3b Execution D4 Flow Applicability Song 2022 ⚠️ Some ⚠️ Some 🔴 High 🔴 High ✅ Low ⚠️ Some Yao Yao 2025 ⚠️ Some ⚠️ Some 🔴 High ⚠️ Some ✅ Low ⚠️ Some Gonzalez 2025 ⚠️ Some ⚠️ Some 🔴 High ⚠️ Some ✅ Low ⚠️ Some Xu 2026 ⚠️ Some ⚠️ Some 🔴🔴 Very High 🔴 High ✅ Low 🔴 High Yang 2025 ⚠️ Some 🔴 High 🔴🔴 Very High 🔴 High ✅ Low ⚠️ Some Additional Declarations No competing interests reported. Supplementary Files PRISMADTAChecklistSupplementary1FINAL.pdf Supplementary Materials The PRISMA-DTA 2018 checklist is provided as Supplementary File 1. Appendix1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9579591","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":633050441,"identity":"d2d311a5-4460-496d-81de-650cc82b1b1d","order_by":0,"name":"Siye Zhang¹","email":"","orcid":"","institution":"Hunan Provincial People’s Hospital (The First Hospital Affiliated with Hunan Normal University)","correspondingAuthor":false,"prefix":"","firstName":"Siye","middleName":"","lastName":"Zhang¹","suffix":""},{"id":633050442,"identity":"d26f0707-c138-486e-8e76-11a75eb61142","order_by":1,"name":"Lulu Zhang¹","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYDCCAwxsIEqGgZ2B8cGHCgk5eWK18DAwMzAbzjhjYWzYQIIWNmnetopEoAh+wHf7+LMHP3fU8hgc5jE2nDlPIoGxgfnhoxt4tEieS0g37D1zHKTF8MHHbRJ57AxsxsY5eLQYnGE4JsHbdgxqyzaJYsYGHjZp/FoY2yT/QrSYSfPOkUhsOEBQCzPI1zVQLQ1EaJE8w8YmLdt2gEfyMFux4YxjEsaGzQT8wneG/Znk27Y6Ob7jzRsffKipk5Nnb374GJ8WKDiMxGYmrBwE6ohTNgpGwSgYBSMTAACEm0mqZP8axwAAAABJRU5ErkJggg==","orcid":"","institution":"Hunan Provincial People’s Hospital (The First Hospital Affiliated with Hunan Normal University)","correspondingAuthor":true,"prefix":"","firstName":"Lulu","middleName":"","lastName":"Zhang¹","suffix":""}],"badges":[],"createdAt":"2026-04-30 16:24:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9579591/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9579591/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108410007,"identity":"4f71d0d3-b4bf-42aa-a9a0-0cdb0269f3d9","added_by":"auto","created_at":"2026-05-04 10:04:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1164246,"visible":true,"origin":"","legend":"\u003cp\u003ePRISMA-DTA 2020 flow diagram of the systematic review of speckle-tracking echocardiography diagnostic accuracy in sepsis-induced cardiomyopathy. Of 128 unique records screened, 105 were excluded at title/abstract and 18 at full-text assessment. Five studies were retained for descriptive synthesis, including one with severe incorporation bias was reported with explicit caveat.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9579591/v1/282773bcea8a2e42044ff25a.png"},{"id":108494709,"identity":"a5781a14-3e48-451b-8326-1ff3d2d195d8","added_by":"auto","created_at":"2026-05-05 10:06:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1166331,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9579591/v1/9ebe95aa-6ed6-40d8-ba16-dcdbd2d5b2f7.pdf"},{"id":108492746,"identity":"6a0e44e1-4edf-429a-a610-a32bd903ecbe","added_by":"auto","created_at":"2026-05-05 09:58:30","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":43273,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Materials\u003c/p\u003e\n\u003cp\u003eThe PRISMA-DTA 2018 checklist is provided as Supplementary File 1.\u003c/p\u003e","description":"","filename":"PRISMADTAChecklistSupplementary1FINAL.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9579591/v1/e1227b51355964955d8658a2.pdf"},{"id":108493503,"identity":"8f1b2c87-b60d-4192-b2a5-a4134e9e85ce","added_by":"auto","created_at":"2026-05-05 10:00:45","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":14399,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9579591/v1/5e14f2d9fd0e5a528b8dffdc.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Sepsis-Induced Cardiomyopathy and Speckle Tracking Echocardiography: A Systematic Review of Diagnostic Accuracy and the Reference Standard Problem","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSepsis-induced cardiomyopathy (SICM) occurs in approximately 20% of sepsis cases and independently predicts mortality [1]. Unlike ischemic injury, SICM is typically reversible - contractile function recovers in survivors without permanent structural damage [1, 17]. Early identification of myocardial dysfunction during sepsis is therefore critical for risk stratification and therapeutic tailoring.\u003c/p\u003e\n\u003cp\u003eSpeckle tracking echocardiography (STE) quantifies myocardial deformation and has been proposed as a more sensitive, load-independent measure than conventional echocardiography [2]. Global longitudinal strain (GLS) detects subclinical systolic dysfunction before overt LVEF reduction [3], and strain abnormalities in septic patients with preserved LVEF are associated with increased mortality [4]. These findings have generated interest in STE as both a diagnostic and prognostic tool for SICM.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHowever, rigorous evaluation of diagnostic test accuracy requires a valid reference standard\u003c/strong\u003e - a principle central to diagnostic research [5, 6]. In SICM, no such standard exists. The condition lacks a consensus definition, and its hallmark (reversible, non-ischemic biventricular dysfunction) inherently resists binary classification [7, 18]. LVEF - the most commonly used reference standard - is load-dependent and may pseudo-normalize in the vasodilated septic state [8, 20]. Cardiac biomarkers (troponins, natriuretic peptides) are frequently elevated in critical illness from non-cardiac causes, limiting specificity [7, 19]. Composite criteria are inconsistently defined across studies. This \u003cstrong\u003ereference standard problem\u003c/strong\u003e has fundamental consequences: if the standard used to classify SICM is itself of uncertain validity, estimates of sensitivity and specificity risk being circular rather than cumulative [5, 6].\u003c/p\u003e\n\u003cp\u003eWe therefore conducted a systematic review with two aims: (1) to catalogue available diagnostic evidence for STE in SICM/RVD, and (2) to critically appraise the reference standards employed. \u003cstrong\u003eUnderlying this review is a premise that we will explicitly test: the diagnostic accuracy dilemma in SICM is not primarily a statistical problem-it is an epistemological one.\u003c/strong\u003e In the absence of a load-independent, sepsis-validated gold standard, every candidate test (STE, biomarkers, cardiac MRI) is evaluated against proxies that share the same fundamental limitation. The resulting heterogeneity across studies, we propose, is not noise to be averaged away but a structural inevitability.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eWe followed PRISMA-DTA 2018 [9] and registered a protocol (PROSPERO CRD420261371085). PubMed/MEDLINE and Cochrane Library were searched from inception to April 16, 2025. To supplement coverage, we additionally searched OpenAlex (https://openalex.org), an open bibliographic database indexing over 250 million scholarly works from PubMed Central, Crossref, DOAJ, and institutional repositories. Five search queries across OpenAlex returned 1,450 records; three candidate titles were evaluated in full, and none represented additional diagnostic accuracy studies meeting inclusion criteria. (Embase, Scopus, and Web of Science were not searched due to institutional access limitations.) Reference lists of included studies were reviewed; systematic forward citation searching (snowballing) was not performed.\u003c/p\u003e\n\u003cp\u003eEligible studies evaluated diagnostic accuracy of STE-derived parameters for SICM or sepsis-associated RVD in adults (≥18 years), with a prespecified threshold or diagnostic model, reporting sensitivity/specificity or AUROC. No language or date restrictions were applied. Only peer-reviewed full-text publications were included; conference abstracts, preprints, and unpublished data were excluded. Exclusions: feasibility studies, prognostic studies without diagnostic discrimination, animal/pediatric, case reports, reviews.\u003c/p\u003e\n\u003cp\u003eTwo reviewers independently screened, extracted data, and assessed risk of bias using an \u003cstrong\u003eexpanded QUADAS-2\u003c/strong\u003e tool [10] with separate domains for reference standard \u003cstrong\u003edefinition validity\u003c/strong\u003e (Domain 3a) and \u003cstrong\u003eexecution independence\u003c/strong\u003e (Domain 3b). Disagreements were resolved by discussion or arbitration by a third reviewer (L.Z.). A standardized data extraction form was piloted on two randomly selected included studies and refined before full extraction. Authors were not contacted for missing data because all required information was available in the published reports. Data extraction captured: (a) study design and clinical setting; (b) sepsis definition used; (c) index test parameter(s) and prespecified threshold(s); (d) reference standard definition and its rationale; (e) diagnostic accuracy metrics (AUROC, sensitivity, specificity); and (f) sample size. SICM was recorded as defined by each study’s reference standard; no uniform SICM definition was imposed a priori. Incorporation bias (index test embedded in reference standard) was rated “very high risk”. Certainty of evidence was graded using GRADE-DTA [11]. Given anticipated heterogeneity, meta-analysis was pre-specified only if ≥3 studies shared an identical, load-independent reference standard - a scenario we judged unlikely. Descriptive synthesis followed SWiM guidelines [12]. Because no two studies used the same index test against the same reference standard, meta-analysis was not performed. Instead, we grouped studies by reference standard strategy (Table 2) and reported diagnostic accuracy estimates narratively. Between-study variability in index test parameters, thresholds, and reference standards was described as a structural feature rather than statistical noise.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eStudy selection.\u003c/strong\u003e Of 128 citations, 105 were excluded at title/abstract. Of the 23 remaining full-text-assessed studies, 8 were excluded, and 15 reported both an STE index test and a stated reference standard (the “broad diagnostic evidence base”) (Figure 1). Of these, five were retained for the main descriptive synthesis; six lacked a suitable reference standard, one was a feasibility study, and three exhibited severe incorporation bias (mathematically circular).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCharacteristics of included studies\u003c/strong\u003e (Table 1). All five were single-center (four China, one Portugal). Sample sizes 98-181. Index tests: TMADmid [Song 2022], RVTMADmid [Yao Yao 2025], myocardial work indices (GWI, GCW) and GLS [Xu 2026], comparison of two operational definitions [Gonzalez 2025], and a three-variable prediction model (GLS + E + TAPSE) [Yang 2025]. No two studies used the same index test against the same reference standard.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReference standard strategies\u003c/strong\u003e (Table 2): 1. \u003cstrong\u003eLoad-dependent conventional parameters\u003c/strong\u003e (Song 2022: LVEF \u0026lt; 50%; Yao Yao 2025: FAC \u0026lt; 35% or TAPSE \u0026lt; 16 mm [13]). 2. \u003cstrong\u003eExpert composite diagnosis\u003c/strong\u003e (Yang 2025: senior intensivist judgment based on dynamic echocardiography, ventricular dysfunction criteria, and exclusion of ischemia). 3. \u003cstrong\u003eOperational criteria comparison without external validation\u003c/strong\u003e (Gonzalez 2025: STE-based vs. non-STE definition). 4. \u003cstrong\u003eIncorporation bias\u003c/strong\u003e (Xu 2026: composite reference LVEF \u0026lt; 50% \u003cstrong\u003eor\u003c/strong\u003e GLS \u0026lt; -17%, embedding the STE index test within the reference standard).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiagnostic performance.\u003c/strong\u003e Song 2022: AUROC \u0026gt; 0.90 for TMADmid (sens 83.7%, spec 71.1%) against LVEF \u0026lt; 50%. Yao Yao 2025: AUROC 0.913 (95% CI 0.866-0.960) for RVTMADmid (cut-off \u0026lt;10.95 mm; sens 80.4%, spec 86.3%). Gonzalez 2025: STE-based standard identified SIMD in 71.4% vs. 58.2% by non-STE standard; no AUROC reported. Xu 2026: AUROCs 0.77-0.81 for GWI/GCW, but estimates are mathematically circular due to incorporation bias. Yang 2025: AUROC 0.879 (training) and 0.888 (validation) for the three-variable model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRisk of bias (QUADAS-2).\u003c/strong\u003e Domain 3a (reference standard definition) was \u003cstrong\u003ehigh or very high risk\u003c/strong\u003e across all five studies (Table 3). Yang 2025 also had high risk in Domain 2 (index test threshold derived from the same dataset). Domain 4 (flow/timing) was low risk universally.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCertainty of evidence (GRADE-DTA).\u003c/strong\u003e For all five diagnostic comparisons, evidence certainty was judged \u003cstrong\u003every low\u003c/strong\u003e (downgraded for serious risk of bias, inconsistency, indirectness, and imprecision).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003ePrincipal findings.\u003c/strong\u003e Only five studies provide diagnostic accuracy data for STE in SICM that meet minimal inclusion criteria. Reported AUROCs range from 0.77 to \u0026gt;0.90, but \u003cstrong\u003eno two studies used the same index test against the same reference standard\u003c/strong\u003e, and \u003cstrong\u003enone used a reference standard independently validated as a gold standard for SICM in septic populations\u003c/strong\u003e. \u003cstrong\u003eThis is not a statistical nuisance that larger sample sizes or random-effects meta-analysis could resolve.\u003c/strong\u003e The heterogeneity is structural because the field lacks what diagnostic research fundamentally requires: a reference standard that is pathophysiologically grounded, load-independent, and feasible in sepsis. Without it, every test is chasing a ghost-and every accuracy estimate is anchored to a proxy of uncertain validity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe reference standard problem: a taxonomy of clinical strategies.\u003c/strong\u003e Each strategy serves a different purpose and carries different limitations.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLoad-dependent conventional parameters\u003c/em\u003e (LVEF, FAC, TAPSE) are the clinical currency of bedside cardiac assessment. Their load-dependence is well recognized; clinicians interpret them with context. However, when used as a research benchmark, they inherit the uncertainty of pseudo-normalization. Song 2022’s AUROC \u0026gt; 0.90 tells us that TMADmid predicts LVEF \u0026lt; 50% - not that it detects “true” SICM. Yao Yao 2025’s 0.913 AUROC similarly reflects prediction of conventional RV parameters in ventilated patients, not detection of intrinsic myocardial dysfunction.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eExpert composite diagnosis\u003c/em\u003e (Yang 2025) mirrors actual bedside practice: an intensivist synthesizing serial echocardiography, multiple domains, and exclusion of ischemia. This is clinically authentic, but methodologically constrained. The AUROC measures the model’s ability to replicate expert opinion - valuable for prediction, but not diagnostic accuracy against an objective gold standard. Moreover, deriving the threshold from the same dataset (internal circularity) overestimates performance.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eOperational criteria comparison\u003c/em\u003e (Gonzalez 2025) asks not “which is correct?” but “do different definitions identify different phenotypes?” The STE-based definition identified more patients with lower recovery rates - a clinically meaningful observation. However, without an external validator, we cannot know whether the additional cases are true positives or false positives.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eIncorporation bias\u003c/em\u003e (Xu 2026) is a mathematical pitfall. Defining the reference standard as LVEF \u0026lt; 50% \u003cstrong\u003eor\u003c/strong\u003e GLS \u0026lt; -17% embeds the STE index test into the reference standard, guaranteeing overestimation. The reported AUROCs (0.77-0.81) should not be interpreted as unbiased diagnostic accuracy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBeyond STE: the reference standard dilemma is domain-wide — and circular.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo appreciate that this is not a problem unique to STE, consider high‑sensitivity troponin I (hs‑TnI). It achieves an AUROC of approximately 0.67 [14] for diagnosing SICM — a level of discrimination that falls well short of standalone diagnostic test expectations. \u003cstrong\u003eBut the central question is not whether hs‑TnI detects \u003cem\u003esomething\u003c/em\u003e in sepsis — it clearly does — but whether what it detects corresponds to the entity we call SICM.\u003c/strong\u003e The reference standards used to validate hs‑TnI are themselves echocardiographic: LVEF, wall motion score index. These are the same load‑dependent surrogates that limit the specificity of any single parameter. The biomarker is being judged against the same flawed proxies against which STE is judged. \u003cstrong\u003eThis is circularity, not cumulative validation.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNatriuretic peptides face analogous constraints [15] from volume status, afterload, and renal function. Cardiac MRI — often proposed as a “true” gold standard — is rarely feasible in septic patients (transport, breath‑hold, contrast safety in acute kidney injury) and, even if feasible, lacks validated SICM‑specific thresholds. What remains after examining all modalities? \u003cstrong\u003eA circular library: STE is validated against echo parameters; echo parameters are validated against biomarkers; biomarkers are validated against echo parameters. No modality escapes the absence of a load‑independent, sepsis‑validated anchor.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis is the epistemological core of the problem. The heterogeneity across studies is not a correctable statistical artefact; it is the inevitable consequence of evaluating multiple tests against multiple imperfect proxies without a single external validator. No amount of statistical correction — latent class analysis [16], composite reference standards, Bayesian adjustment — can conjure a gold standard into existence when the disease construct itself lacks a consensus definition. \u003cstrong\u003eThe field’s next advance will not come from a larger AUC or a more sensitive strain parameter. It will come from agreeing on what SICM \u003cem\u003eis\u003c/em\u003e — and building a reference standard that does not move with the load.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical implications.\u003c/strong\u003e \u003cem\u003eCannot\u003c/em\u003e conclude: (1) ranking of STE parameters by diagnostic accuracy (different reference standards, different clinical tasks); (2) universal diagnostic thresholds (any threshold is reference-standard-specific); (3) any single STE parameter as a standalone diagnostic test for SICM. \u003cem\u003eCan\u003c/em\u003e conclude: (1) STE parameters add incremental value in \u003cstrong\u003emultimodal assessment\u003c/strong\u003e - e.g., preserved LVEF but abnormal GLS together with diastolic and right-sided parameters; (2) \u003cstrong\u003edynamic monitoring\u003c/strong\u003e (trajectory over 48-72 hours) conveys more information than a single threshold; (3) STE provides \u003cstrong\u003eclinically actionable information\u003c/strong\u003e at the bedside where CMR is infeasible - not as a definitive diagnostic test, but as a sensitive monitor of myocardial function trajectory.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFuture directions.\u003c/strong\u003e Progress likely requires (a) \u003cstrong\u003ecomposite prediction models\u003c/strong\u003e integrating echocardiographic and non-echocardiographic data (biomarkers, lactate clearance, vasopressor trajectories) with \u003cstrong\u003eexternal validation\u003c/strong\u003e; (b) \u003cstrong\u003edynamic diagnostic criteria\u003c/strong\u003e that explicitly incorporate emergence, multi-domain involvement, and documented reversibility; and (c) a \u003cstrong\u003emultidisciplinary consensus\u003c/strong\u003e on core phenotypic elements of SICM - not to designate a gold standard from existing tools, but to define what the disease \u003cem\u003eis\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrengths and limitations.\u003c/strong\u003e Strengths: first systematic review of STE diagnostic accuracy for SICM that centers reference standard quality as the primary analytic focus; pre-specified meta-analysis only for identical load-independent standards, avoiding forced quantitative synthesis. Limitations: single-center studies (four China, one Portugal); search limited to PubMed/MEDLINE, Cochrane Library, and OpenAlex due to institutional access constraints (Embase, Scopus, and Web of Science not searched). Across all three searched sources, 1,586 records were identified; after deduplication, 128 unique records were screened, of which 105 were excluded at title/abstract and 18 at full text, leaving five studies for synthesis. OpenAlex supplementary searching (1,450 records screened, three candidate titles evaluated in full) yielded no additional eligible diagnostic accuracy studies, supporting the assessment that PubMed/Cochrane coverage is sufficient for this niche topic. Systematic forward citation searching (snowballing) was not performed, though reference lists of included studies were reviewed and yielded no additional records. Small number of included studies; aggregate data only; reference standard validity assessed by reasoning, not independent empirical validation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion.\u003c/strong\u003e The evidence base for STE diagnostic accuracy in SICM is anchored to reference standards of uncertain validity. Reported AUROCs describe discrimination against conventional echocardiographic thresholds, expert judgments, or mutually referential criteria - not against a validated gold standard. Heterogeneity is structural, not statistical. Progress may depend less on finding a more sensitive echocardiographic parameter than on establishing a robust, pathophysiology-grounded disease definition - one that is load-independent, feasible at the bedside, and validated across clinical contexts. Until a load-independent, sepsis-validated disease definition is established, \u003cstrong\u003ethe diagnostic accuracy of any single test - STE, troponin, CMR - remains anchored to proxies that are themselves unvalidated.\u003c/strong\u003e This is not a statistical problem awaiting a better meta-analysis. It is an epistemological problem: without a gold standard, we cannot know what we are measuring, and without knowing what we are measuring, claims of diagnostic accuracy are circular. STE should therefore be understood as a \u003cstrong\u003evaluable adjunct to clinical assessment\u003c/strong\u003e - a sensitive monitor of myocardial function trajectory - rather than a definitive diagnostic test for a condition whose boundaries remain debated.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eSICM = sepsis-induced cardiomyopathy; RVD = right ventricular dysfunction; STE = speckle tracking echocardiography; GLS = global longitudinal strain; LVEF = left ventricular ejection fraction; FAC = fractional area change; TAPSE = tricuspid annular plane systolic excursion; TMAD = tissue motion annular displacement; AUROC = area under the receiver operating characteristic curve; CI = confidence interval; QUADAS-2 = Quality Assessment of Diagnostic Accuracy Studies-2; GRADE-DTA = Grading of Recommendations Assessment, Development and Evaluation for Diagnostic Test Accuracy; SWiM = Synthesis Without Meta-analysis; PRISMA-DTA = Preferred Reporting Items for Systematic Reviews and Meta-Analyses of Diagnostic Test Accuracy; SIMD = sepsis-induced myocardial dysfunction; GWI = global work index; GCW = global constructive work\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eS.Z. and L.Z. conceived and designed the study. S.Z. performed the systematic search, screening, data extraction, risk-of-bias assessment, and drafted the original manuscript. L.Z. supervised the study, validated the methodology, and critically revised the manuscript. Both authors reviewed and approved the final version.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eAll data supporting this systematic review are contained within the published articles cited in the manuscript and are available from the corresponding author upon reasonable request\u003c/p\u003e\n\u003ch2\u003eEthics Approval and Consent to Participate\u003c/h2\u003e\n\u003cp\u003eNot applicable. This systematic review analyzed published aggregate data; no human subjects were directly involved.\u003c/p\u003e\n\u003ch2\u003eConsent for Publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003e\u0026nbsp;\u003c/h2\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis study received no external funding.\u003c/p\u003e\n\u003ch2\u003eCompeting Interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eHasegawa D, Ishisaka Y, Maeda T, Prasitlumkum N, Nishida K, Dugar S, et al. Prevalence and Prognosis of Sepsis-Induced Cardiomyopathy: A Systematic Review and Meta-Analysis. \u003cem\u003eJ Intensive Care Med\u003c/em\u003e. 2023;38(9):797-808. DOI: 10.1177/08850666231180526. PMID: 37168766. |\u003c/li\u003e\n \u003cli\u003eMondillo S, Galderisi M, Mele D, Cameli M, Lomoriello VS, Zaca V, et al. Speckle-Tracking Echocardiography: A New Technique for Assessing Myocardial Function.\u0026nbsp;\u003cem\u003eJ Ultrasound Med\u003c/em\u003e. 2011;30(1):71-83. DOI: 10.7863/jum.2011.30.1.71. PMID: 21196899. |\u003c/li\u003e\n \u003cli\u003eMihos CG, Liu JE, Anderson KM, Pernetz MA, O\u0026rsquo;Driscoll JM, Aurigemma GP, et al. Speckle-Tracking Strain Echocardiography for the Assessment of Left Ventricular Structure and Function: A Scientific Statement From the American Heart Association.\u0026nbsp;\u003cem\u003eCirculation\u003c/em\u003e. 2025;152(10):e96-e109. DOI: 10.1161/CIR.0000000000001354. PMID: 40505503. |\u003c/li\u003e\n \u003cli\u003eSanfilippo F, Corredor C, Fletcher N, Tritapepe L, Lorini FL, Arcadipane A, et al. Left Ventricular Systolic Function Evaluated by Strain Echocardiography and Relationship with Mortality in Patients with Severe Sepsis or Septic Shock: A Systematic Review and Meta-Analysis.\u0026nbsp;\u003cem\u003eCrit Care\u003c/em\u003e. 2018;22(1):183. DOI: 10.1186/s13054-018-2113-y. PMID: 30012173. |\u003c/li\u003e\n \u003cli\u003eWhiting 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.\u0026nbsp;\u003cem\u003eBMC Med Res Methodol\u003c/em\u003e. 2003;3:25. DOI: 10.1186/1471-2288-3-25. PMID: 12857518. |\u003c/li\u003e\n \u003cli\u003eDendukuri N, Schiller I, de Groot J, Libman M, Moons K, Reitsma J, van Smeden M. Concerns About Composite Reference Standards in Diagnostic Research.\u0026nbsp;\u003cem\u003eBMJ\u003c/em\u003e. 2018;360:j5779. DOI: 10.1136/bmj.j5779. PMID: 29348126. |\u003c/li\u003e\n \u003cli\u003eZakynthinos GE, Magira EE, Theordorakopoulou M, Zakynthinos SG. Septic Cardiomyopathy: Difficult Definition, Challenging Diagnosis, Unclear Treatment.\u0026nbsp;\u003cem\u003eJ Clin Med\u003c/em\u003e. 2025;14(3):986. DOI: 10.3390/jcm14030986. PMID: 39939997. |\u003c/li\u003e\n \u003cli\u003eRepess\u0026eacute; X, Charron C, Vieillard-Baron A. Evaluation of Left Ventricular Systolic Function Revisited in Septic Shock.\u0026nbsp;\u003cem\u003eCrit Care\u003c/em\u003e. 2013;17:164. DOI: 10.1186/cc12755. PMID: 23849948. |\u003c/li\u003e\n \u003cli\u003eMcInnes MDF, Moher D, Thombs BD, McGrath TA, Bossuyt PM, et al. Preferred Reporting Items for a Systematic Review and Meta-Analysis of Diagnostic Test Accuracy Studies: The PRISMA-DTA Statement.\u0026nbsp;\u003cem\u003eJAMA\u003c/em\u003e. 2018;319(4):388-396. DOI: 10.1001/jama.2017.19163. PMID: 29340590. |\u003c/li\u003e\n \u003cli\u003e Whiting PF, Rutjes AWS, Westwood ME, Mallett S, Deeks JJ, Reitsma JB, et al. QUADAS-2: A Revised Tool for the Quality Assessment of Diagnostic Accuracy Studies.\u0026nbsp;\u003cem\u003eAnn Intern Med\u003c/em\u003e. 2011;155(8):529-536. DOI: 10.7326/0003-4819-155-8-201110180-00009. PMID: 22007046. |\u003c/li\u003e\n \u003cli\u003e Sch\u0026uuml;nemann HJ, Mustafa RA, Brożek J, Santesso N, Meerpohl JJ, Leeflang M. GRADE Guidelines 22: The GRADE Approach for Tests and Strategies \u0026mdash; From Test Accuracy to Patient-Important Outcomes and Recommendations.\u0026nbsp;\u003cem\u003eJ Clin Epidemiol\u003c/em\u003e. 2019;111:69-82. DOI: 10.1016/j.jclinepi.2019.02.003. PMID: 30784589. |\u003c/li\u003e\n \u003cli\u003e Campbell M, McKenzie JE, Sowden A, Katikireddi SV, Brennan SE, Ellis S, et al. Synthesis Without Meta-Analysis (SWiM) in Systematic Reviews: Reporting Guideline.\u0026nbsp;\u003cem\u003eBMJ\u003c/em\u003e. 2020;368:l6890. DOI: 10.1136/bmj.l6890. PMID: 31948937. |\u003c/li\u003e\n \u003cli\u003e Rudski LG, Lai WW, Afilalo J, Hua L, Handschumacher MD, Chandrasekaran K, et al. Guidelines for the Echocardiographic Assessment of the Right Heart in Adults: A Report From the American Society of Echocardiography.\u0026nbsp;\u003cem\u003eJ Am Soc Echocardiogr\u003c/em\u003e. 2010;23(7):685-713. DOI: 10.1016/j.echo.2010.05.010. PMID: 20620859. |\u003c/li\u003e\n \u003cli\u003e Kim JS, Kim M, Kim YJ, Ryoo SM, Sohn CH, Ahn S, et al. Troponin Testing for Assessing Sepsis-Induced Myocardial Dysfunction in Patients with Septic Shock.\u0026nbsp;\u003cem\u003eJ Clin Med\u003c/em\u003e. 2019;8(2):239. DOI: 10.3390/jcm8020239. PMID: 30759844. |\u003c/li\u003e\n \u003cli\u003e Cavefors O, Einarsson F, Holmqvist J, et al. Cardiac Biomarkers for Screening and Prognostication of Cardiac Dysfunction in Critically Ill Patients.\u0026nbsp;\u003cem\u003eESC Heart Fail\u003c/em\u003e. 2024;11(6):4009-4018. DOI: 10.1002/ehf2.14980. PMID: 39087599. |\u003c/li\u003e\n \u003cli\u003e Reitsma JB, Rutjes AWS, Khan KS, Coomarasamy A, Bossuyt PMM. A Review of Solutions for Diagnostic Accuracy Studies With an Imperfect or Missing Reference Standard.\u0026nbsp;\u003cem\u003eJ Clin Epidemiol\u003c/em\u003e. 2009;62(8):797-806. DOI: 10.1016/j.jclinepi.2009.02.005. PMID: 19447581. |\u003c/li\u003e\n \u003cli\u003e Parker MM, Suffredini AF, Natanson C, Ognibene FP, Shelhamer JH, Parrillo JE. Profound but Reversible Myocardial Depression in Patients with Septic Shock.\u0026nbsp;\u003cem\u003eAnn Intern Med\u003c/em\u003e. 1984;100(4):483-490. DOI: 10.7326/0003-4819-100-4-483. PMID: 6703075. |\u003c/li\u003e\n \u003cli\u003e L\u0026rsquo;Heureux M, Sternberg M, Brath L, Turlington J, Kashiouris MG. Sepsis-Induced Cardiomyopathy: A Comprehensive Review.\u0026nbsp;\u003cem\u003eCurr Cardiol Rep\u003c/em\u003e. 2020;22(5):35. DOI: 10.1007/s11886-020-01277-2. PMID: 32377972. |\u003c/li\u003e\n \u003cli\u003e Landesberg G, Jaffe AS, Gilon D, et al. Troponin Elevation in Severe Sepsis and Septic Shock: The Role of Left Ventricular Diastolic Dysfunction and Right Ventricular Dilatation.\u0026nbsp;\u003cem\u003eCrit Care Med\u003c/em\u003e. 2014;42(4):790-800. DOI: 10.1097/CCM.0000000000000107. PMID: 24365861. |\u003c/li\u003e\n \u003cli\u003e Hollenberg SM. Sepsis-Associated Cardiomyopathy: Long-Term Prognosis, Management, and Guideline-Directed Medical Therapy. \u003cem\u003eCurr Cardiol Rep\u003c/em\u003e. 2025;27:5. DOI: 10.1007/s11886-024-02175-7. PMID: 39776326. |\u003c/li\u003e\n \u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Characteristics of Included Studies\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStudy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCountry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDesign（n）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIndex Test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReference Standard\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAUROC (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSong 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRetrospective;n=NR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTMADmid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLVEF \u0026lt; 50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;0.90 (NR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e83.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e71.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYao Yao 2025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRetrospective;n=98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRVTMADmid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFAC \u0026lt; 35% or TAPSE \u0026lt; 16 mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.913 (0.866-0.960)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e80.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e86.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eXu 2026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eProspective;n=NR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGWI, GCW, GLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLVEF \u0026lt; 50% \u003cstrong\u003eor\u003c/strong\u003e GLS \u0026lt; -17%*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.77-0.81 (NR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGonzalez 2025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePortugal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eProspective; n=NR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSTE composite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOperational definition (STE vs non-STE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNR\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYang 2025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eProspective; n=181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGLS + E + TAPSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eExpert composite diagnosis\u0026Dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.879 [train] / 0.888 [val]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cpre\u003eNR = not reported. *Sepsis definitions were not reported in any included study.\u003c/pre\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Reference Standard Strategies\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" class=\"fr-table-selection-hover\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStrategy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStudy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDefinition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDiagnostic Question\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKey Limitation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQUADAS-2 D3a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1: Conventional parameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSong 2022; Yao Yao 2025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLVEF \u0026lt;50% (SICM); FAC \u0026lt;35% or TAPSE \u0026lt;16 mm (RVD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDoes STE predict conventional dysfunction?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLoad-dependent; pseudo-normalization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2: Expert composite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYang 2025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDynamic echo + dysfunction criteria + ischemia exclusion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDoes multimodal model predict expert-diagnosed SICM?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSubjective; unstandardized\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVery High\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3: Operational comparison\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGonzalez 2025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSTE composite vs non-STE composite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWhat is concordance between two definitions?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo external validator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4: Incorporation bias\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eXu 2026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLVEF \u0026lt;50% \u003cstrong\u003eor\u003c/strong\u003e GLS \u0026lt;-17%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNot applicable (circular design)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMathematical circularity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVery High*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*\u0026ldquo;Very High\u0026rdquo; signals irrecoverable accuracy estimates regardless of sample size.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. QUADAS-2 Assessments\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eStudy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eD1 Selection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eD2 Index Test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eD3a Ref Std\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eD3b Execution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eD4 Flow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eApplicability\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSong 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e⚠️\u0026nbsp;Some\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e⚠️\u0026nbsp;Some\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e🔴\u0026nbsp;High\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e🔴\u0026nbsp;High\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e✅\u0026nbsp;Low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e⚠️\u0026nbsp;Some\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYao Yao 2025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e⚠️\u0026nbsp;Some\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e⚠️\u0026nbsp;Some\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e🔴\u0026nbsp;High\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e⚠️\u0026nbsp;Some\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e✅\u0026nbsp;Low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e⚠️\u0026nbsp;Some\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGonzalez 2025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e⚠️\u0026nbsp;Some\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e⚠️\u0026nbsp;Some\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e🔴\u0026nbsp;High\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e⚠️\u0026nbsp;Some\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e✅\u0026nbsp;Low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e⚠️\u0026nbsp;Some\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eXu 2026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e⚠️\u0026nbsp;Some\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e⚠️\u0026nbsp;Some\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e🔴🔴\u0026nbsp;Very High\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e🔴\u0026nbsp;High\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e✅\u0026nbsp;Low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e🔴\u0026nbsp;High\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYang 2025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e⚠️\u0026nbsp;Some\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e🔴\u0026nbsp;High\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e🔴🔴\u0026nbsp;Very High\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e🔴\u0026nbsp;High\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e✅\u0026nbsp;Low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e⚠️ Some\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9579591/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9579591/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground.\u003c/strong\u003eSepsis-induced cardiomyopathy (SICM) is common but lacks a consensus definition. Speckle tracking echocardiography (STE) offers load-independent deformation imaging and has been proposed as a diagnostic tool. However, diagnostic accuracy estimates depend critically on the reference standard used, and no gold standard for SICM has been validated in septic populations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjectives.\u003c/strong\u003e To catalogue diagnostic accuracy evidence for STE-derived parameters in SICM and right ventricular dysfunction (RVD), and to critically appraise the reference standards employed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods.\u003c/strong\u003e Systematic review (PRISMA-DTA) of PubMed/MEDLINE and Cochrane Library (inception to April 16, 2025). Two reviewers screened, extracted data, and assessed risk of bias (expanded QUADAS-2 with Domain 3a/3b). Certainty of evidence was rated by GRADE-DTA. Descriptive synthesis (SWiM) was used; meta-analysis was pre-specified only if ≥3 studies shared an identical load-independent reference standard.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults.\u003c/strong\u003e Of 128 citations, 15 reported both an STE index test and a stated reference standard; 5 were retained for descriptive synthesis (4 from China, 1 from Portugal; n = 98-181). Reported AUROCs ranged from 0.77 to \u0026gt;0.90. No two studies used the same index test against the same reference standard. Four reference standard strategies were used: load-dependent conventional parameters (LVEF, FAC, TAPSE), expert composite diagnosis, operational criteria comparison, and incorporation bias (index test embedded in reference standard). QUADAS-2 Domain 3a (reference standard definition) was high/very high risk in all five studies. Certainty of evidence for all diagnostic comparisons was \u003cstrong\u003every low\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions.\u003c/strong\u003e The apparent diagnostic signal of STE for SICM is anchored to reference standards of uncertain validity in septic populations. Heterogeneity is structural, not statistical. Progress may depend less on finding a more sensitive echocardiographic parameter than on establishing a robust, pathophysiology-grounded disease definition. Until then, STE should be considered a valuable adjunct to clinical assessment rather than a definitive diagnostic test.\u003c/p\u003e","manuscriptTitle":"Sepsis-Induced Cardiomyopathy and Speckle Tracking Echocardiography: A Systematic Review of Diagnostic Accuracy and the Reference Standard Problem","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-04 10:04:38","doi":"10.21203/rs.3.rs-9579591/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ceb01f12-473d-41aa-9afd-fc3489ee1dcc","owner":[],"postedDate":"May 4th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-04T13:29:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-04T06:04:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-05-04T06:03:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Critical Care","date":"2026-04-30T16:16:58+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T13:40:45+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-04 10:04:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9579591","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9579591","identity":"rs-9579591","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}