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Böhnke J, Zapf A, Kramer K, Weber P, Karch A, Rübsamen N. Diagnostic test accuracy in longitudinal study settings: theoretical approaches with use cases from clinical practice. J Clin Epidemiol 2024; 169:111314. [PMID: 38432525 DOI: 10.1016/j.jclinepi.2024.111314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 02/21/2024] [Accepted: 02/26/2024] [Indexed: 03/05/2024]
Abstract
OBJECTIVES In this study, we evaluate how to estimate diagnostic test accuracy (DTA) correctly in the presence of longitudinal patient data (ie, repeated test applications per patient). STUDY DESIGN AND SETTING We used a nonparametric approach to estimate the sensitivity and specificity of three tests for different target conditions with varying characteristics (ie, episode length and disease-free intervals between episodes): 1) systemic inflammatory response syndrome (n = 36), 2) depression (n = 33), and 3) epilepsy (n = 30). DTA was estimated on the levels 'time', 'block', and 'patient-time' for each diagnosis, representing different research questions. The estimation was conducted for the time units per minute, per hour, and per day. RESULTS A comparison of DTA per and across use cases showed variations in the estimates, which resulted from the used level, the time unit, the resulting number of observations per patient, and the diagnosis-specific characteristics. Intra- and inter-use-case comparisons showed that the time-level had the highest DTA, particularly the larger the time unit, and that the patient-time-level approximated 50% sensitivity and specificity. CONCLUSION Researchers need to predefine their choices (ie, estimation levels and time units) based on their individual research aims, estimands, and diagnosis-specific characteristics of the target outcomes to make sure that unbiased and clinically relevant measures are communicated. In cases of uncertainty, researchers could report the DTA of the test using more than one estimation level and/or time unit.
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Affiliation(s)
- Julia Böhnke
- Institute of Epidemiology and Social Medicine, University of Münster, Albert-Schweitzer-Campus 1, 48149 Münster, Germany.
| | - Antonia Zapf
- Department of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Katharina Kramer
- Mathematical Statistics and Artificial Intelligence in Medicine, University of Augsburg, Augsburg, Germany
| | - Philipp Weber
- Department of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - André Karch
- Institute of Epidemiology and Social Medicine, University of Münster, Albert-Schweitzer-Campus 1, 48149 Münster, Germany
| | - Nicole Rübsamen
- Institute of Epidemiology and Social Medicine, University of Münster, Albert-Schweitzer-Campus 1, 48149 Münster, Germany
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Schefzik R, Hahn B, Schneider-Lindner V. Dissecting contributions of individual systemic inflammatory response syndrome criteria from a prospective algorithm to the prediction and diagnosis of sepsis in a polytrauma cohort. Front Med (Lausanne) 2023; 10:1227031. [PMID: 37583420 PMCID: PMC10424878 DOI: 10.3389/fmed.2023.1227031] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 07/17/2023] [Indexed: 08/17/2023] Open
Abstract
Background Sepsis is the leading cause of death in intensive care units (ICUs), and its timely detection and treatment improve clinical outcome and survival. Systemic inflammatory response syndrome (SIRS) refers to the concurrent fulfillment of at least two out of the following four clinical criteria: tachycardia, tachypnea, abnormal body temperature, and abnormal leukocyte count. While SIRS was controversially abandoned from the current sepsis definition, a dynamic SIRS representation still has potential for sepsis prediction and diagnosis. Objective We retrospectively elucidate the individual contributions of the SIRS criteria in a polytrauma cohort from the post-surgical ICU of University Medical Center Mannheim (Germany). Methods We used a dynamic and prospective SIRS algorithm tailored to the ICU setting by accounting for catecholamine therapy and mechanical ventilation. Two clinically relevant tasks are considered: (i) sepsis prediction using the first 24 h after admission to our ICU, and (ii) sepsis diagnosis using the last 24 h before sepsis onset and a time point of comparable ICU treatment duration for controls, respectively. We determine the importance of individual SIRS criteria by systematically varying criteria weights when summarizing the SIRS algorithm output with SIRS descriptors and assessing the classification performance of the resulting logistic regression models using a specifically developed ranking score. Results Our models perform better for the diagnosis than the prediction task (maximum AUROC 0.816 vs. 0.693). Risk models containing only the SIRS level average mostly show reasonable performance across criteria weights, with prediction and diagnosis AUROCs ranging from 0.455 (weight on leukocyte criterion only) to 0.693 and 0.619 to 0.800, respectively. For sepsis prediction, temperature and tachypnea are the most important SIRS criteria, whereas the leukocytes criterion is least important and potentially even counterproductive. For sepsis diagnosis, all SIRS criteria are relevant, with the temperature criterion being most influential. Conclusion SIRS is relevant for sepsis prediction and diagnosis in polytrauma, and no criterion should a priori be omitted. Hence, the original expert-defined SIRS criteria are valid, capturing important sepsis risk determinants. Our prospective SIRS algorithm provides dynamic determination of SIRS criteria and descriptors, allowing their integration in sepsis risk models also in other settings.
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Affiliation(s)
- Roman Schefzik
- Department of Anesthesiology and Surgical Intensive Care Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
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Böhnke J, Varghese J, Karch A, Rübsamen N. Systematic review identifies deficiencies in reporting of diagnostic test accuracy among clinical decision support systems. J Clin Epidemiol 2022; 151:171-184. [PMID: 35987404 DOI: 10.1016/j.jclinepi.2022.08.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 07/21/2022] [Accepted: 08/10/2022] [Indexed: 12/25/2022]
Abstract
OBJECTIVES This systematic review assesses the reporting quality and risk of bias in studies evaluating the diagnostic test accuracy (DTA) of clinical decision support systems (CDSS). STUDY DESIGN AND SETTING The Cochrane Library, PubMed/MEDLINE, Scopus, and Web of Science were searched for studies, published between January 1, 2016 and May 31, 2021, evaluating the DTA of CDSS for human patients. Articles using a patient's self-diagnosis, assessing disease severity, focusing on treatment/follow-up, or comparing pre-post CDSS implementation periods were excluded. All eligible studies were assessed for reporting quality using STARD 2015 and for risk of bias using QUADAS-2. Item ratings were presented using heat maps. This study was reported as per PRISMA-DTA. RESULTS In total, 158 of 2,820 screened articles were included in the analysis. The studies were heterogeneous in terms of study characteristics, reporting quality, risk of biases, and applicability concerns with few highly rated studies. Mostly the overall quality was deficient for items addressing the domains 'methodology,' 'results,' and 'other information'. CONCLUSION Our analysis revealed shortcomings in critical domains of reporting quality and risk of bias, indicating the need for additional guidance and training in an interdisciplinary scientific field with mixed biostatistical expertise.
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Affiliation(s)
- Julia Böhnke
- University of Münster, Institute of Epidemiology and Social Medicine, Münster, Germany.
| | - Julian Varghese
- University of Münster, Institute of Medical Informatics, Münster, Germany
| | - André Karch
- University of Münster, Institute of Epidemiology and Social Medicine, Münster, Germany
| | - Nicole Rübsamen
- University of Münster, Institute of Epidemiology and Social Medicine, Münster, Germany
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Lingam A, Koppolu P, Akhter F, Afroz M, Tabassum N, Arshed M, Khan T, ElHaddad S. Future trends of artificial intelligence in dentistry. JOURNAL OF NATURE AND SCIENCE OF MEDICINE 2022. [DOI: 10.4103/jnsm.jnsm_2_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
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Wulff A, Montag S, Rübsamen N, Dziuba F, Marschollek M, Beerbaum P, Karch A, Jack T. Clinical evaluation of an interoperable clinical decision-support system for the detection of systemic inflammatory response syndrome in critically ill children. BMC Med Inform Decis Mak 2021; 21:62. [PMID: 33602206 PMCID: PMC7889709 DOI: 10.1186/s12911-021-01428-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Accepted: 02/03/2021] [Indexed: 11/11/2022] Open
Abstract
Background Systemic inflammatory response syndrome (SIRS) is defined as a non-specific inflammatory process in the absence of infection. SIRS increases susceptibility for organ dysfunction, and frequently affects the clinical outcome of affected patients. We evaluated a knowledge-based, interoperable clinical decision-support system (CDSS) for SIRS detection on a pediatric intensive care unit (PICU). Methods The CDSS developed retrieves routine data, previously transformed into an interoperable format, by using model-based queries and guideline- and knowledge-based rules. We evaluated the CDSS in a prospective diagnostic study from 08/2018–03/2019. 168 patients from a pediatric intensive care unit of a tertiary university hospital, aged 0 to 18 years, were assessed for SIRS by the CDSS and by physicians during clinical routine. Sensitivity and specificity (when compared to the reference standard) with 95% Wald confidence intervals (CI) were estimated on the level of patients and patient-days. Results Sensitivity and specificity was 91.7% (95% CI 85.5–95.4%) and 54.1% (95% CI 45.4–62.5%) on patient level, and 97.5% (95% CI 95.1–98.7%) and 91.5% (95% CI 89.3–93.3%) on the level of patient-days. Physicians’ SIRS recognition during clinical routine was considerably less accurate (sensitivity of 62.0% (95% CI 56.8–66.9%)/specificity of 83.3% (95% CI 80.4–85.9%)) when measurd on the level of patient-days. Evaluation revealed valuable insights for the general design of the CDSS as well as specific rule modifications. Despite a lower than expected specificity, diagnostic accuracy was higher than the one in daily routine ratings, thus, demonstrating high potentials of using our CDSS to help to detect SIRS in clinical routine. Conclusions We successfully evaluated an interoperable CDSS for SIRS detection in PICU. Our study demonstrated the general feasibility and potentials of the implemented algorithms but also some limitations. In the next step, the CDSS will be optimized to overcome these limitations and will be evaluated in a multi-center study. Trial registration: NCT03661450 (ClinicalTrials.gov); registered September 7, 2018. Supplementary Information The online version contains supplementary material available at 10.1186/s12911-021-01428-7.
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Affiliation(s)
- Antje Wulff
- Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Karl-Wiechert-Allee 3, 30625, Hannover, Germany.
| | - Sara Montag
- Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Karl-Wiechert-Allee 3, 30625, Hannover, Germany.
| | - Nicole Rübsamen
- Institute of Epidemiology and Social Medicine, University of Muenster, Domagkstr. 3, 48149, Muenster, Germany
| | - Friederike Dziuba
- Department of Pediatric Cardiology and Intensive Care Medicine, Hannover Medical School, Carl-Neuberg-Str. 1, 30625, Hannover, Germany
| | - Michael Marschollek
- Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Karl-Wiechert-Allee 3, 30625, Hannover, Germany
| | - Philipp Beerbaum
- Department of Pediatric Cardiology and Intensive Care Medicine, Hannover Medical School, Carl-Neuberg-Str. 1, 30625, Hannover, Germany
| | - André Karch
- Institute of Epidemiology and Social Medicine, University of Muenster, Domagkstr. 3, 48149, Muenster, Germany
| | - Thomas Jack
- Department of Pediatric Cardiology and Intensive Care Medicine, Hannover Medical School, Carl-Neuberg-Str. 1, 30625, Hannover, Germany
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Abstract
OBJECTIVES This survey aimed to review aspects of clinical decision support (CDS) that contribute to burnout and identify key themes for improving the acceptability of CDS to clinicians, with the goal of decreasing said burnout. METHODS We performed a survey of relevant articles from 2018-2019 addressing CDS and aspects of clinician burnout from PubMed and Web of Science™. Themes were manually extracted from publications that met inclusion criteria. RESULTS Eighty-nine articles met inclusion criteria, including 12 review articles. Review articles were either prescriptive, describing how CDS should work, or analytic, describing how current CDS tools are deployed. The non-review articles largely demonstrated poor relevance and acceptability of current tools, and few studies showed benefits in terms of efficiency or patient outcomes from implemented CDS. Encouragingly, multiple studies highlighted steps that succeeded in improving both acceptability and relevance of CDS. CONCLUSIONS CDS can contribute to clinician frustration and burnout. Using the techniques of improving relevance, soliciting feedback, customization, measurement of outcomes and metrics, and iteration, the effects of CDS on burnout can be ameliorated.
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Affiliation(s)
- Ivana Jankovic
- Division of Endocrinology, Stanford University School of Medicine, Stanford, CA, USA
| | - Jonathan H. Chen
- Center for Biomedical Informatics Research and Division of Hospital Medicine, Stanford University School of Medicine, Stanford, CA, USA
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State of the art in clinical decision support applications in pediatric perioperative medicine. Curr Opin Anaesthesiol 2020; 33:388-394. [DOI: 10.1097/aco.0000000000000850] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
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