Decision Support Tool for Early Differential Diagnosis of Acute Lung Injury and Cardiogenic Pulmonary Edema in Medical Critically Ill Patients

Schmickl, Christopher N.; Shahjehan, Khurram; Li, Guangxi; Dhokarh, Rajanigandha; Kashyap, Rahul; Janish, Christopher; Alsara, Anas; Jaffe, Allan S.; Hubmayr, Rolf D.; Gajić, Ognjen

doi:10.1378/chest.11-1496

Cited by 20 publications

(45 citation statements)

References 31 publications

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“…Twenty-five studies involved academic institutions, three involved community hospitals and five studies were performed in a mixed community and academic setting. A thematic abundance of ESS systems was identified, covering a wide spectrum of clinical areas encompassing acute lung injury [13][14][15], patient-ventilator interaction [16][17][18], identification of seizures [19], rapid patient deterioration [20] nosocomial infection surveillance [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35], heart failure [36], life-threatening electrocardiographic changes [37][38][39] and hemodynamic stability monitoring [40][41][42].…”

Section: Resultsmentioning

confidence: 99%

Diagnostic Performance of Electronic Syndromic Surveillance Systems in Acute Care

Kashiouris

O’Horo

Pickering³

et al. 2013

Appl Clin Inform

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SummaryContext: Healthcare Electronic Syndromic Surveillance (ESS) is the systematic collection, analysis and interpretation of ongoing clinical data with subsequent dissemination of results, which aid clinical decision-making. Objective:To evaluate, classify and analyze the diagnostic performance, strengths and limitations of existing acute care ESS systems. Data Sources: All available to us studies in Ovid MEDLINE, Ovid EMBASE, CINAHL and Scopus databases, from as early as January 1972 through the first week of September 2012. Study Selection: Prospective and retrospective trials, examining the diagnostic performance of inpatient ESS and providing objective diagnostic data including sensitivity, specificity, positive and negative predictive values. Data Extraction: Two independent reviewers extracted diagnostic performance data on ESS systems, including clinical area, number of decision points, sensitivity and specificity. Positive and negative likelihood ratios were calculated for each healthcare ESS system. A likelihood matrix summarizing the various ESS systems performance was created. Results: The described search strategy yielded 1639 articles. Of these, 1497 were excluded on abstract information. After full text review, abstraction and arbitration with a third reviewer, 33 studies met inclusion criteria, reporting 102,611 ESS decision points. The yielded I2 was high (98.8%), precluding meta-analysis. Performance was variable, with sensitivities ranging from 21% -100% and specificities ranging from 5%-100%. Conclusions: There is significant heterogeneity in the diagnostic performance of the available ESS implements in acute care, stemming from the wide spectrum of different clinical entities and ESS systems. Based on the results, we introduce a conceptual framework using a likelihood ratio matrix for evaluation and meaningful application of future, frontline clinical decision support systems.

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Section: Resultsmentioning

confidence: 99%

Diagnostic Performance of Electronic Syndromic Surveillance Systems in Acute Care

Kashiouris

O’Horo

Pickering³

et al. 2013

Appl Clin Inform

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“…While we await the results of preventative trials to guide the management of the at-risk patient, the bedside clinician can still use the LIPS and other tools 30 to identify patients at risk of ARDS and perform interventions that may decrease such risk. For example, a patient determined to be at high risk for development of ARDS may benefit from the earlier initiation of resuscitation and antibiotics for severe sepsis 20 , a more conservative fluid strategy early in their ICU course 31 , and lung-protective ventilation even in the absence of ARDS, an intervention that has been associated with improved clinical outcomes 13,14,32 .…”

Section: Identifying Patients At-risk Of Developing Ardsmentioning

confidence: 99%

Approach to the Patient with the Acute Respiratory Distress Syndrome

Janz¹,

Ware²

2014

Clinics in Chest Medicine

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Given the high incidence and mortality of ARDS in critically ill patients, every practitioner needs a bedside approach both for early identification of patients at risk for ARDS and for the appropriate evaluation of patients who meet the diagnostic criteria of ARDS. Recent advances such as the Lung Injury Prediction Score, the Early Acute Lung Injury score, and validation of the SpO2/FiO2 ratio for assessing the degree of hypoxemia are all practical tools to aid the practitioner in caring for patients at risk of ARDS and will likely become more important in the future as more preventative therapies for ARDS are investigated. For patients who meet the diagnostic criteria for ARDS, the practitioner should focus on a thorough search for an underlying cause as well as the concurrent possibility of an underlying disease process that mimics the clinical syndrome of ARDS.

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“…In addition to identification of disease for clinical support, CDS tools integrated into existing EHR or databases can rapidly identify patients for inclusion into research studies [80] . Utilization of CDS in this way has the potential to increase recruitment numbers, especially among studies with time dependent inclusion criteria.…”

Section: Potential For Cds In Critical Carementioning

confidence: 99%

Computerized decision support in adult and pediatric critical care

Williams¹,

Bratton²

2013

WJCCM

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Computerized decision support (CDS) is the most advanced form of clinical decision support available and has evolved with innovative technologies to provide meaningful assistance to medical professionals. Critical care clinicians are in unique environments where vast amounts of data are collected on individual patients, and where expedient and accurate decisions are paramount to the delivery of quality healthcare. Many CDS tools are in use today among adult and pediatric intensive care units as diagnostic aides, safety alerts, computerized protocols, and automated recommendations for management. Some CDS use have significantly decreased adverse events and improved costs when carefully implemented and properly operated. CDS tools integrated into electronic health records are also valuable to researchers providing rapid identification of eligible patients, streamlining data-gathering and analysis, and providing cohorts for study of rare and chronic diseases through data-warehousing. Although the need for human judgment in the daily care of critically ill patients has limited the study and realization of meaningful improvements in overall patient outcomes, CDS tools continue to evolve and integrate into the daily workflow of clinicians, and will likely provide advancements over time. Through novel technologies, CDS tools have vast potential for progression and will significantly impact the field of critical care and clinical research in the future.

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Decision Support Tool for Early Differential Diagnosis of Acute Lung Injury and Cardiogenic Pulmonary Edema in Medical Critically Ill Patients

Cited by 20 publications

References 31 publications

Diagnostic Performance of Electronic Syndromic Surveillance Systems in Acute Care

Diagnostic Performance of Electronic Syndromic Surveillance Systems in Acute Care

Approach to the Patient with the Acute Respiratory Distress Syndrome

Computerized decision support in adult and pediatric critical care

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