Appl Clin Inform - Towards prevention of acute syndromes: electronic identification of at-risk patients during hospital admission.

Tópicos

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Resumo

CKGROUND: Identifying patients at risk for acute respiratory distress syndrome (ARDS) before their admission to intensive care is crucial to prevention and treatment. The objective of this study is to determine the performance of an automated algorithm for identifying selected ARDS predisposing conditions at the time of hospital admission.METHODS: This secondary analysis of a prospective cohort study included 3,005 patients admitted to hospital between January 1 and December 31, 2010. The automated algorithm for five ARDS predisposing conditions (sepsis, pneumonia, aspiration, acute pancreatitis, and shock) was developed through a series of queries applied to institutional electronic medical record databases. The automated algorithm was derived and refined in a derivation cohort of 1,562 patients and subsequently validated in an independent cohort of 1,443 patients. The sensitivity, specificity, and positive and negative predictive values of an automated algorithm to identify ARDS risk factors were compared with another two independent data extraction strategies, including manual data extraction and ICD-9 code search. The reference standard was defined as the agreement between the ICD-9 code, automated and manual data extraction.RESULTS: Compared to the reference standard, the automated algorithm had higher sensitivity than manual data extraction for identifying a case of sepsis (95% vs. 56%), aspiration (63% vs. 42%), acute pancreatitis (100% vs. 70%), pneumonia (93% vs. 62%) and shock (77% vs. 41%) with similar specificity except for sepsis and pneumonia (90% vs. 98% for sepsis and 95% vs. 99% for pneumonia). The PPV for identifying these five acute conditions using the automated algorithm ranged from 65% for pneumonia to 91 % for acute pancreatitis, whereas the NPV for the automated algorithm ranged from 99% to 100%.CONCLUSION: A rule-based electronic data extraction can reliably and accurately identify patients at risk of ARDS at the time of hospital admission.

Resumo Limpo

ckground identifi patient risk acut respiratori distress syndrom ard admiss intens care crucial prevent treatment object studi determin perform autom algorithm identifi select ard predispos condit time hospit admissionmethod secondari analysi prospect cohort studi includ patient admit hospit januari decemb autom algorithm five ard predispos condit sepsi pneumonia aspir acut pancreat shock develop seri queri appli institut electron medic record databas autom algorithm deriv refin deriv cohort patient subsequ valid independ cohort patient sensit specif posit negat predict valu autom algorithm identifi ard risk factor compar anoth two independ data extract strategi includ manual data extract icd code search refer standard defin agreement icd code autom manual data extractionresult compar refer standard autom algorithm higher sensit manual data extract identifi case sepsi vs aspir vs acut pancreat vs pneumonia vs shock vs similar specif except sepsi pneumonia vs sepsi vs pneumonia ppv identifi five acut condit use autom algorithm rang pneumonia acut pancreat wherea npv autom algorithm rang conclus rulebas electron data extract can reliabl accur identifi patient risk ard time hospit admiss

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