INTRODUCTION: Existing prediction rules for prospectively prognosticating early mortality following pulmonary embolism (PE) require clinical and/or laboratory data, and are rarely suitable for claims database analyses. We sought to develop a claims-based prediction rule that retrospectively classifies PE patients into low- or higher-risk in-hospital mortality categories. MATERIALS AND METHODS: We randomly assigned MarketScan database patient admitted for PE between April 2010 and September 2013 into derivation (80%) and validation (20%) cohorts. A prediction rule (In-hospital Mortality for PulmonAry embolism using Claims daTa or IMPACT) was derived using multivariable logistic regression, with in-hospital mortality as the dependent variable and demographic/comorbidity data available in claims databases as independent variables. In-hospital mortality rates for low- and higher-risk patients were compared across the derivation and validation cohorts, and prediction rule performance was assessed by evaluating sensitivity and specificity estimates. RESULTS: A total of 27,833 patients admitted for PE were included. The IMPACT rule consisted of 12 risk factors, and categorized 46% of patients as low-risk in both cohorts. Patients classified as low-risk by IMPACT (defined as an estimated in-hospital mortality risk ≤1.5%) had average in-hospital mortality rates of 0.81% (95% confidence interval [CI], 0.65-1.00) in the derivation and 0.77% (95% CI, 0.50-1.18) in the validation cohort. Higher-risk patients had average in-hospital mortality rates of 4.61% (95% CI, 4.25-5.01) and 5.02% (95% CI, 4.30-5.85), respectively. The IMPACT rule had high sensitivity for classifying in-hospital mortality risk (0.87 in both cohorts), but moderate specificity (0.47 for both cohorts). LIMITATIONS: We were unable to assess 30 day mortality as an endpoint. IMPACT was only validated in an internal sample. CONCLUSIONS: The IMPACT prediction rule may be able to retrospectively classify PE patients' in-hospital mortality risk with high sensitivity and moderate specificity.
INTRODUCTION: Existing prediction rules for prospectively prognosticating early mortality following pulmonary embolism (PE) require clinical and/or laboratory data, and are rarely suitable for claims database analyses. We sought to develop a claims-based prediction rule that retrospectively classifies PE patients into low- or higher-risk in-hospital mortality categories. MATERIALS AND METHODS: We randomly assigned MarketScan database patient admitted for PE between April 2010 and September 2013 into derivation (80%) and validation (20%) cohorts. A prediction rule (In-hospital Mortality for PulmonAry embolism using Claims daTa or IMPACT) was derived using multivariable logistic regression, with in-hospital mortality as the dependent variable and demographic/comorbidity data available in claims databases as independent variables. In-hospital mortality rates for low- and higher-risk patients were compared across the derivation and validation cohorts, and prediction rule performance was assessed by evaluating sensitivity and specificity estimates. RESULTS: A total of 27,833 patients admitted for PE were included. The IMPACT rule consisted of 12 risk factors, and categorized 46% of patients as low-risk in both cohorts. Patients classified as low-risk by IMPACT (defined as an estimated in-hospital mortality risk ≤1.5%) had average in-hospital mortality rates of 0.81% (95% confidence interval [CI], 0.65-1.00) in the derivation and 0.77% (95% CI, 0.50-1.18) in the validation cohort. Higher-risk patients had average in-hospital mortality rates of 4.61% (95% CI, 4.25-5.01) and 5.02% (95% CI, 4.30-5.85), respectively. The IMPACT rule had high sensitivity for classifying in-hospital mortality risk (0.87 in both cohorts), but moderate specificity (0.47 for both cohorts). LIMITATIONS: We were unable to assess 30 day mortality as an endpoint. IMPACT was only validated in an internal sample. CONCLUSIONS: The IMPACT prediction rule may be able to retrospectively classify PE patients' in-hospital mortality risk with high sensitivity and moderate specificity.
Authors: Christine G Kohn; Erin R Weeda; Neela Kumar; Philip S Wells; W Frank Peacock; Gregory J Fermann; Li Wang; Onur Baser; Jeff R Schein; Concetta Crivera; Craig I Coleman Journal: Intern Emerg Med Date: 2017-02-09 Impact factor: 3.397
Authors: Erin R Weeda; Philip S Wells; W Frank Peacock; Gregory J Fermann; Christopher W Baugh; Veronica Ashton; Concetta Crivera; Peter Wildgoose; Jeff R Schein; Craig I Coleman Journal: Intern Emerg Med Date: 2016-10-18 Impact factor: 3.397
Authors: Craig I Coleman; Christine G Kohn; Concetta Crivera; Jeffrey R Schein; W Frank Peacock Journal: BMJ Open Date: 2015-10-28 Impact factor: 2.692
Authors: Craig I Coleman; W Frank Peacock; Gregory J Fermann; Concetta Crivera; Erin R Weeda; Michael Hull; Mary DuCharme; Laura Becker; Jeff R Schein Journal: BMC Health Serv Res Date: 2016-10-22 Impact factor: 2.655
Authors: Elaine Nguyen; Craig I Coleman; W Frank Peacock; Philip S Wells; Erin R Weeda; Veronica Ashton; Concetta Crivera; Peter Wildgoose; Jeff R Schein; Thomas J Bunz; Gregory J Fermann Journal: BMC Pulm Med Date: 2017-02-13 Impact factor: 3.317
Authors: Erin R Weeda; Christine G Kohn; Gregory J Fermann; W Frank Peacock; Christopher Tanner; Daniel McGrath; Concetta Crivera; Jeff R Schein; Craig I Coleman Journal: Thromb J Date: 2016-03-14