Laboratory tests to identify patients at risk of early major adverse events: a prospective pilot study

Kaufman, Melissa; Bebee, Bronwyn; Bailey, James M.; Robbins, Rebecca J.; Hart, Graeme K; Bellomo, Rinaldo

doi:10.1111/imj.12509

Cited by 5 publications

(3 citation statements)

References 38 publications

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“…[7][8][9][10] We chose to analyze laboratory tests that are commonly ordered early during hospital admission (only laboratory tests ordered in at least 80% of patients upon hospital admission at UMMC) and that have been associated with severity of illness (imminent death, in-hospital mortality, adverse events, etc.). [11][12][13][14][15][16][17][18] Laboratory values selected included hemoglobin, platelet count, leucocyte count, blood urea nitrogen (BUN), serum creatinine, glucose, sodium, potassium, and total bicarbonate (HCO 3 ). As our purpose was to assess severity of illness at admission, laboratory tests were only included if they were ordered within 24 h of admission.…”

Section: Study Cohort and Databasementioning

confidence: 99%

Admission Laboratory Values Accurately Predict In-hospital Mortality: a Retrospective Cohort Study

et al. 2019

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BACKGROUND:The greater the severity of illness of a patient, the more likely the patient will have a poor hospital outcome. However, hospital-wide severity of illness scores that are simple, widely available, and not diagnosis-specific are still needed. Laboratory tests could potentially be used as an alternative to estimate severity of illness. OBJECTIVE: To evaluate the ability of hospital laboratory tests, as measures of severity of illness, to predict inhospital mortality among hospitalized patients, and therefore, their potential as an alternative method to severity of illness risk adjustment. DESIGNS AND PATIENTS: A retrospective cohort study among 38,367 adult non-trauma patients admitted to the University of Maryland Medical Center between November 2015 and November 2017 was performed. Laboratory tests (hemoglobin, platelet count, white blood cell count, urea nitrogen, creatinine, glucose, sodium, potassium, and total bicarbonate (HCO 3 )) were included when ordered within 24 h from the time of hospital admission. A multivariable logistic regression model to predict inhospital mortality was constructed using a section of our cohort (n = 21,003). MAIN MEASURES: Model performance was evaluated using the c-statistic and the Hosmer-Lemeshow (HL) test. In addition, a calibration belt was constructed to determine a confidence interval around the calibration curve with the purpose of identifying ranges of miscalibration. KEY RESULTS: Patient age and all laboratory tests predicted mortality with good discrimination (c = 0.79). Patients with abnormal HCO 3 levels or leukocyte counts at admission were twice as likely to die during their hospital stay as patients with normal results. A good model calibration and fit were observed (HL = 13.9, p = 0.18). CONCLUSIONS: Admission laboratory tests are able to predict in-hospital mortality with good accuracy, providing an objective and widely accessible approach to severity of illness risk adjustment.

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Section: Study Cohort and Databasementioning

confidence: 99%

Admission Laboratory Values Accurately Predict In-hospital Mortality: a Retrospective Cohort Study

et al. 2019

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“…The future is here with several promising wireless devices and the possibility to combine various physiological and patient characteristics [60], to give an unprecedented precise risk estimation of adverse postsurgical events. However, detection and prediction is only going half the way.…”

Section: In Conclusionmentioning

confidence: 99%

Technological aided assessment of the acutely ill patient – The case of postoperative complications

Haahr-Raunkjær

Meyhoff

Sørensen

et al. 2017

European Journal of Internal Medicine

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Surgical interventions come with complications and highly reported mortality after major surgery. The mortality may be a result of delayed detection of severe complications due to lower monitoring frequency in the general wards. Several studies have shown that continuous monitoring is superior to the manually intermittent recorded monitoring in terms of detecting abnormal physiological signs. Hopefully improved observations may result in earlier detection and clinical intervention. This narrative review will describe current monitoring possibilities for postoperative patients and how it may prevent complications. Several wireless systems are being developed for monitoring vital parameters, but many of these are not yet validated for critically ill patients. The ultimate goal with patient monitoring and detect of events is to prevent postoperative complications, death and costs in the health care system. A few studies indicate that monitoring systems detect deteriorating patients earlier than the nurses, and this was associated with less clinical instability. An important caveat of future devices is to assess their effect in relevant patient populations and not only in healthy test-subjects. Implementation of novel technologies is expensive although expected to be cost-effective if just few adverse events can be prevented. The future is here with promising devices and the possibility to give an unprecedented precise risk estimation of adverse post-surgical events. Next step is to integrate existing evidence based treatment algorithms to demonstrate the clinical efficacy of implementing the new technology.

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“…Strategies to optimise use of continuous monitoring systems in noncritical care areas are needed. In addition, laboratory results may provide a ''biochemical trigger'' that predicts increased risk of unplanned ICU admission and in-patient death [5].…”

Section: Improving Detection Of Deteriorationmentioning

confidence: 99%

What’s new with rapid response systems?

et al. 2014

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Laboratory tests to identify patients at risk of early major adverse events: a prospective pilot study

Abstract: Commonly performed laboratory tests identify surgical ward patients at risk of early major adverse events. Further studies are needed to assess whether such identification system can be used to trigger interventions that help improve patient outcomes.

Cited by 5 publications

References 38 publications

Admission Laboratory Values Accurately Predict In-hospital Mortality: a Retrospective Cohort Study

Admission Laboratory Values Accurately Predict In-hospital Mortality: a Retrospective Cohort Study

Technological aided assessment of the acutely ill patient – The case of postoperative complications

What’s new with rapid response systems?

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