Non-essential blood tests in the intensive care unit: a prospective observational study

Mikhaeil, Michael; Day, Andrew G.; Ilan, Roy

doi:10.1007/s12630-016-0793-9

Cited by 17 publications

(21 citation statements)

References 11 publications

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“…[1][2][3][4] Targeted diagnostic testing, in which clinicians order tests in response to specific clinical questions, may reduce unnecessary diagnostic test ordering by up to 50% and has been identified as a priority area to decrease waste by both clinicians and consumers. 5,6 However, concern has been raised that targeted testing may increase the risk of missed diagnoses, and high-quality evidence of the safety and effectiveness of targeted compared with routine diagnostic testing is lacking. 7,8 The primary aim of this systematic review was to evaluate interventional studies reporting on the implementation of a targeted diagnostic testing strategy in adult patients admitted to the ICU.…”

Section: Introductionmentioning

confidence: 99%

Safety and efficacy of routine diagnostic test reduction interventions in patients admitted to the intensive care unit: A systematic review and meta-analysis

Hooper

Anstey

Litton

2021

Anaesth Intensive Care

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Reducing unnecessary routine diagnostic testing has been identified as a strategy to curb wasteful healthcare. However, the safety and efficacy of targeted diagnostic testing strategies are uncertain. The aim of this study was to systematically review interventions designed to reduce pathology and chest radiograph testing in patients admitted to the intensive care unit (ICU). A predetermined protocol and search strategy included OVID MEDLINE, OVID EMBASE and the Cochrane Central Register of Controlled Trials from inception until 20 November 2019. Eligible publications included interventional studies of patients admitted to an ICU. There were no language restrictions. The primary outcomes were in-hospital mortality and test reduction. Key secondary outcomes included ICU mortality, length of stay, costs and adverse events. This systematic review analysed 26 studies (with more than 44,00 patients) reporting an intervention to reduce one or more diagnostic tests. No studies were at low risk of bias. In-hospital mortality, reported in seven studies, was not significantly different in the post-implementation group (829 of 9815 patients, 8.4%) compared with the pre-intervention group (1007 of 9848 patients, 10.2%), (relative risk 0.89, 95% confidence intervals 0.79 to 1.01, P = 0.06, I2 39%). Of the 18 studies reporting a difference in testing rates, all reported a decrease associated with targeted testing (range 6%–72%), with 14 (82%) studies reporting >20% reduction in one or more tests. Studies of ICU targeted test interventions are generally of low quality. The majority report substantial decreases in testing without evidence of a significant difference in hospital mortality.

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Section: Introductionmentioning

confidence: 99%

Safety and efficacy of routine diagnostic test reduction interventions in patients admitted to the intensive care unit: A systematic review and meta-analysis

Hooper

Anstey

Litton

2021

Anaesth Intensive Care

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“…The new features also improved balanced metrics such as F 1 score, AUC, mean G, and IBA, thus showing the benefit of the inclusion of such types of features to improve the capacity for discriminating between normal and abnormal test results. For instance, approach 2 improved the aforementioned metrics for blood gas tests (potential of hydrogen and PCO 2 ), which are among the most expensive laboratory blood tests ordered in the ICU [ 9 ].…”

Section: Discussionmentioning

confidence: 99%

“…Previous reports have underscored overtesting in ICUs. In a study conducted in an ICU of a tertiary hospital in Ontario, Canada, physicians retrospectively analyzed 694 blood tests performed over 4 weeks and concluded that only 48.7% of those tests were essential [ 9 ]. A similar pattern was found in a Brazilian ICU, in which approximately half (1768/3622, 48.81%) blood tests performed over 2 months resulted in normal values [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Predicting Abnormal Laboratory Blood Test Results in the Intensive Care Unit Using Novel Features Based on Information Theory and Historical Conditional Probability: Observational Study

Valderrama¹,

Niven²,

Stelfox³

et al. 2022

JMIR Med Inform

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Background Redundancy in laboratory blood tests is common in intensive care units (ICUs), affecting patients’ health and increasing health care expenses. Medical communities have made recommendations to order laboratory tests more judiciously. Wise selection can rely on modern data-driven approaches that have been shown to help identify low-yield laboratory blood tests in ICUs. However, although conditional entropy and conditional probability distribution have shown the potential to measure the uncertainty of yielding an abnormal test, no previous studies have adapted these techniques to include them in machine learning models for predicting abnormal laboratory test results. Objective This study aimed to address the limitations of previous reports by adapting conditional entropy and conditional probability to extract features for predicting abnormal laboratory blood test results. Methods We used an ICU data set collected across Alberta, Canada, which included 55,689 ICU admissions from 48,672 patients. We investigated the features of conditional entropy and conditional probability by comparing the performances of 2 machine learning approaches for predicting normal and abnormal results for 18 blood laboratory tests. Approach 1 used patients’ vitals, age, sex, and admission diagnosis as features. Approach 2 used the same features plus the new conditional entropy–based and conditional probability–based features. Both approaches used 4 different machine learning models (fuzzy model, logistic regression, random forest, and gradient boosting trees) and 10 metrics (sensitivity, specificity, accuracy, precision, negative predictive value [NPV], F1 score, area under the curve [AUC], precision-recall AUC, mean G, and index balanced accuracy) to assess the performance of the approaches. Results Approach 1 achieved an average AUC of 0.86 for all 18 laboratory tests across the 4 models (sensitivity 78%, specificity 84%, precision 82%, NPV 75%, F1 score 79%, and mean G 81%), whereas approach 2 achieved an average AUC of 0.89 (sensitivity 84%, specificity 84%, precision 83%, NPV 81%, F1 score 83%, and mean G 84%). We found that the inclusion of the new features resulted in significant differences for most of the metrics in favor of approach 2. Sensitivity significantly improved for 8 and 15 laboratory tests across the different classifiers (minimum P<.001 and maximum P=.04). Mean G and index balanced accuracy, which are balanced performance metrics, also improved significantly across the classifiers for 6 to 10 and 6 to 11 laboratory tests. The most relevant feature was the pretest probability feature, which is the probability that a test result was normal when a certain number of consecutive prior tests was already normal. Conclusions The findings suggest that conditional entropy–based features and pretest probability improve the capacity to discriminate between normal and abnormal laboratory test results. Detecting the next laboratory test result is an intermediate step toward developing guidelines for reducing overtesting in the ICU.

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“…Nach einer Metaanalyse beträgt dies im Durchschnitt 43,9 % bei der initialen Vorstellung eines Patienten [4]. In einer weiteren Studie waren Fachärzte der Auffassung, dass nur 48,7 % der angeordneten Laborwerte essenziell waren [5]. Die Laboranordnungen bedeuten, dass mehrfache Blutentnahmen erforderlich sind, die beim kritisch Kranken zu einer iatrogenen Anämie führen [6][7][8][9] und erhebliche Kosten verursachen [4,5,10].…”

Section: Hintergrundunclassified

“…In einer weiteren Studie waren Fachärzte der Auffassung, dass nur 48,7 % der angeordneten Laborwerte essenziell waren [5]. Die Laboranordnungen bedeuten, dass mehrfache Blutentnahmen erforderlich sind, die beim kritisch Kranken zu einer iatrogenen Anämie führen [6][7][8][9] und erhebliche Kosten verursachen [4,5,10]. Eine übermäßige Labordiagnostik birgt schließlich auch das Risiko, von dem wesentlichen Problem abzulenken und folglich essenzielle Therapiemaßnahmen zu verzögern.…”

Section: Hintergrundunclassified

Labordiagnostik in der Intensivmedizin

Petros

Weidhase

2020

Wien klin Mag

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ZusammenfassungDie sorgfältige Anamneseerhebung und die klinische Untersuchung bleiben nach wie vor die wichtigsten diagnostischen Säulen. In der Intensiv- und Notfallmedizin werden zahlreiche Laborparameter erhoben. Die Rate an übermäßigen Laboranordnungen während der Patientenaufnahme beträgt fast 50 %. Oft sind die anamnestischen Angaben unzureichend, um eine gezielte Labordiagnostik vorzunehmen. Die Sorge, etwas zu übersehen, führt auch zu einer erweiterten Diagnostik. Zur Vereinfachung der Behandlungsabläufe werden oft a priori festgelegte Laborprofile benutzt, die symptombezogen angeordnet sind. Zahlreiche Laborparameter sind bei kritisch kranken Patienten außerhalb des Normbereichs. Allerdings wird der Normbereich anhand der Daten gesunder Personen definiert, der jedoch eine klare Unterscheidung zwischen Stressadaptation und klinisch relevanten korrekturbedürftigen Störungen nicht erlaubt. Die pathophysiologischen Veränderungen in vielen Körperregionen infolge der akuten Schädigung, der Reaktion des Organismus und auch der Therapiemaßnahmen führen zu Veränderungen zahlreicher Laborparameter. Ziellose Laborentnahmen tragen zur iatrogenen Anämie und zum Kostenanstieg bei. Außerdem werden die Ergebnisse solcher Entnahmen entweder kaum wahrgenommen oder können im schlimmsten Fall weitere unnötige diagnostische Schritte bzw. eine nicht gerechtfertigte Therapiemaßnahme verursachen. Die Point-of-Care-Labordiagnostik zur Einschätzung der zellulären Homöostase, die die Blutgasanalyse, ein Blutbild sowie die Bestimmung der Serumelektrolyte und des Serumlaktats beinhaltet, sowie die Laborparameter zur Berechnung der intensivmedizinisch relevanten Scores sind einheitlich erforderlich. Darüber hinaus sollte jedoch die Labordiagnostik bezogen auf konkrete klinische Fragestellung klug gewählt werden.

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Non-essential blood tests in the intensive care unit: a prospective observational study

Cited by 17 publications

References 11 publications

Safety and efficacy of routine diagnostic test reduction interventions in patients admitted to the intensive care unit: A systematic review and meta-analysis

Safety and efficacy of routine diagnostic test reduction interventions in patients admitted to the intensive care unit: A systematic review and meta-analysis

Predicting Abnormal Laboratory Blood Test Results in the Intensive Care Unit Using Novel Features Based on Information Theory and Historical Conditional Probability: Observational Study

Labordiagnostik in der Intensivmedizin

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