Browsing by Author "Pendharkar, Sachin R"
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Item Open Access Effectiveness of a standardized electronic admission order set for acute exacerbation of chronic obstructive pulmonary disease(2018-05-30) Pendharkar, Sachin R; Ospina, Maria B; Southern, Danielle A; Hirani, Naushad; Graham, Jim; Faris, Peter; Bhutani, Mohit; Leigh, Richard; Mody, Christopher H; Stickland, Michael KAbstract Background Variation in hospital management of patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD) may prolong length of stay, increasing the risk of hospital-acquired complications and worsening quality of life. We sought to determine whether an evidence-based computerized AECOPD admission order set could improve quality and reduce length of stay. Methods The order set was designed by a provincial COPD working group and implemented voluntarily among three physician groups in a Canadian tertiary-care teaching hospital. The primary outcome was length of stay for patients admitted during order set implementation period, compared to the previous 12 months. Secondary outcomes included length of stay of patients admitted with and without order set after implementation, all-cause readmissions, and emergency department visits. Results There were 556 admissions prior to and 857 admissions after order set implementation, for which the order set was used in 47%. There was no difference in overall length of stay after implementation (median 6.37 days (95% confidence interval 5.94, 6.81) pre-implementation vs. 6.02 days (95% confidence interval 5.59, 6.46) post-implementation, p = 0.26). In the post-implementation period, order set use was associated with a 1.15-day reduction in length of stay (95% confidence interval − 0.5, − 1.81, p = 0.001) compared to patients admitted without the order set. There was no difference in readmissions. Conclusions Use of a computerized guidelines-based admission order set for COPD exacerbations reduced hospital length of stay without increasing readmissions. Interventions to increase order set use could lead to greater improvements in length of stay and quality of care.Item Open Access Granulomatous Pneumocystis Jiroveci Pneumonia Associated with Immune Reconstituted HIV(2011-01-01) Sabur, Natasha F; Kelly, Margaret M; Gill, M John; Ainslie, Martha D; Pendharkar, Sachin RPneumocystis jiroveci pneumonia uncommonly presents with pulmonary nodules and granulomatous inflammation. An unusual case of granulomatous P jiroveci pneumonia in an HIV patient with a CD4+ lymphocyte count of greater than 200 cells/mm3, occurring in the context of immune reconstitution with highly active antiretroviral therapy, is described. The case highlights the importance of establishing this diagnosis to institute appropriate therapy.Item Open Access Use of Electronic Data and Existing Screening Tools to Identify Clinically Significant Obstructive Sleep Apnea(2015-01-01) Severson, Carl A; Pendharkar, Sachin R; Ronksley, Paul E; Tsai, Willis HOBJECTIVES: To assess the ability of electronic health data and existing screening tools to identify clinically significant obstructive sleep apnea (OSA), as defined by symptomatic or severe OSA.METHODS: The present retrospective cohort study of 1041 patients referred for sleep diagnostic testing was undertaken at a tertiary sleep centre in Calgary, Alberta. A diagnosis of clinically significant OSA or an alternative sleep diagnosis was assigned to each patient through blinded independent chart review by two sleep physicians. Predictive variables were identified from online questionnaire data, and diagnostic algorithms were developed. The performance of electronically derived algorithms for identifying patients with clinically significant OSA was determined. Diagnostic performance of these algorithms was compared with versions of the STOP-Bang questionnaire and adjusted neck circumference score (ANC) derived from electronic data.RESULTS: Electronic questionnaire data were highly sensitive (>95%) at identifying clinically significant OSA, but not specific. Sleep diagnostic testing-determined respiratory disturbance index was very specific (specificity ≥95%) for clinically relevant disease, but not sensitive (ud_less_than35%). Derived algorithms had similar accuracy to the STOP-Bang or ANC, but required fewer questions and calculations.CONCLUSIONS: These data suggest that a two-step process using a small number of clinical variables (maximizing sensitivity) and objective diagnostic testing (maximizing specificity) is required to identify clinically significant OSA. When used in an online setting, simple algorithms can identify clinically relevant OSA with similar performance to existing decision rules such as the STOP-Bang or ANC.