Improving prostate cancer detection in veterans through the development of a clinical decision rule for prostate biopsy

BACKGROUND: We sought to improve prostate cancer (PC) detection through developing a prostate biopsy clinical decision rule (PBCDR), based on an elevated PSA and laboratory biomarkers. This decision rule could be used after initial PC screening, providing the patient and clinician information to consider prior to biopsy. METHODS: This case–control study evaluated men from the Tampa, Florida, James A. Haley (JH) Veteran’s Administration (VA) (N = 1,378), from January 1, 1998, through April 15, 2005. To assess the PBCDR we did all of the following: 1) Identified biomarkers that are related to PC and have the capability of improving the efficiency of PC screening; 2) Developed statistical models to determine which can best predict the probability of PC; 3) Compared each potential model to PSA alone using Receiver Operator Characteristic (ROC) curves, to evaluate for improved overall effectiveness in PC detection and reduction in (negative) biopsies; and 4) Evaluated dose–response relationships between specified lab biomarkers (surrogates for extra-prostatic disease development) and PC progression. RESULTS: The following biomarkers were related to PC: hemoglobin (HGB) (OR = 1.42 95% CI 1.27, 1.59); red blood cell (RBC) count (OR = 2.52 95% CI 1.67, 3.78); PSA (OR = 1.04 95% CI 1.03, 1.05); and, creatinine (OR = 1.55 95% CI 1.12, 2.15). Comparing all PC stages versus non-cancerous conditions, the ROC curve area under the curve (AUC) enlarged (increasing the probability of correctly classifying PC): PSA (alone) 0.59 (95% CI 0.55, 0.61); PBCDR model 0.68 (95% CI 0.65, 0.71), and the positive predictive value (PPV) increased: PSA 44.7%; PBCDR model 61.8%. Comparing PC (stages II, III, IV) vs. other, the ROC AUC increased: PSA (alone) 0.63 (95% CI 0.58, 0.66); PBCDR model 0.72 (95% CI 0.68, 0.75), and the PPV increased: 20.6% (PSA); PBCDR model 55.3%. CONCLUSIONS: These results suggest evaluating certain common biomarkers in conjunction with PSA may improve PC prediction prior to biopsy. Moreover, these biomarkers may be more helpful in detecting clinically relevant PC. Follow-up studies should begin with replicating the study on different U.S. VA patients involving multiple practices

Hemoglobin A1c as a Predictor of Incident Diabetes

Objective: Several studies have suggested that $HbA_{1c}$ levels may predict incident diabetes. With new recommendations for use of $HbA_{1c}$ in diagnosing diabetes, many patients with $HbA_{1c}$ results below the diagnostic threshold will be identified. Clinicians will need to categorize risk for a subsequent diabetic diagnosis in such patients. The objective of this study was to determine the ability of $HbA_{1c}$ to predict the incidence of a diabetic diagnosis. Research Design and Methods: We performed a historical cohort study using electronic medical record data from two Department of Veterans Affairs Medical Centers. Patients (n = 12,589) were identified with a baseline $HbA_{1c}$ <6.5% between January 2000 and December 2001 and without a diagnosis of diabetes. Patients (12,375) had at least one subsequent follow-up visit. These patients were tracked for 8 years for a subsequent diagnosis of diabetes. Results: During an average follow-up of 4.4 years, 3,329 (26.9%) developed diabetes. $HbA_{1c}$ $\geq$5.0% carried a significant risk for developing diabetes during follow-up. When compared with the reference group ($HbA_{1c}$ <4.5%), $HbA_{1c}$ increments of 0.5% between 5.0 and 6.4% had adjusted odds ratios of 1.70 (5.0–5.4%), 4.87 (5.5–5.9%), and 16.06 (6.0–6.4%) (P < 0.0001). Estimates of hazard ratios similarly showed significant increases for $HbA_{1c}$ $\geq$5.0%. A risk model for incident diabetes within 5 years was developed and validated using $HbA_{1c}$, age, BMI, and systolic blood pressure. Conclusions: The incidence of diabetes progressively and significantly increased among patients with an $HbA_{1c}$ $\geq$5.0%, with substantially expanded risk for those with $HbA_{1c}$ 6.0–6.4%

Correlates of a Prescription for Bilevel Positive Airway Pressure for Treatment of Obstructive Sleep Apnea among Veterans

Introduction:The acceptance of portable home-based polysomnography together with auto-titrating CPAP has bypassed the need for a laboratory polysomnography. Since bilevel airway pressure (BPAP) is titrated in the sleep lab, patients diagnosed using portable home-based polysomnography may not have the opportunity to receive BPAP. It is unknown whether the patients who would have ordinarily received a BPAP would benefit from it. We determine correlates of receiving BPAP and of being switched from BPAP to CPAP. We examine whether patients with these correlates have better adherence to BPAP versus CPAP. Methods: Retrospective Cohort Study (Correlates at baseline) of 2,513 VA patients with a sleep study between January 2003 and October 2006 and receiving continuous or bilevel positive airway pressure (CPAP [N = 2,251]) or BPAP [N = 262]) by the end of 2007. PAP adherence up to 30 months was assessed. Results: Significant correlates of BPAP were older age (p \u3c 0.001), higher BMI and CHF (p \u3c 0.01), COPD (p \u3c 0.001), higher blood CO2 (p \u3c 0.05), higher AHI and OSA severity (p \u3c 0.001), lower nadir SpO2 (p \u3c 0.001), and greater sleepiness (ESS) (p \u3c 0.01). Patients on BPAP were more adherent to PAP therapy (p \u3c 0.01), but the association largely disappeared following adjustment for BPAP correlates. There was preliminary evidence that these correlates predict long-term adherence to PAP therapy regardless of mode. Conclusions: We identified baseline factors that can help clinicians decide whether to prescribe an auto-BPAP as first-line therapy and that predict good long-term PAP adherence

Improving prostate cancer detection in veterans through the development of a clinical decision rule for prostate biopsy

Abstract Background We sought to improve prostate cancer (PC) detection through developing a prostate biopsy clinical decision rule (PBCDR), based on an elevated PSA and laboratory biomarkers. This decision rule could be used after initial PC screening, providing the patient and clinician information to consider prior to biopsy. Methods This case–control study evaluated men from the Tampa, Florida, James A. Haley (JH) Veteran’s Administration (VA) (N = 1,378), from January 1, 1998, through April 15, 2005. To assess the PBCDR we did all of the following: 1) Identified biomarkers that are related to PC and have the capability of improving the efficiency of PC screening; 2) Developed statistical models to determine which can best predict the probability of PC; 3) Compared each potential model to PSA alone using Receiver Operator Characteristic (ROC) curves, to evaluate for improved overall effectiveness in PC detection and reduction in (negative) biopsies; and 4) Evaluated dose–response relationships between specified lab biomarkers (surrogates for extra-prostatic disease development) and PC progression. Results The following biomarkers were related to PC: hemoglobin (HGB) (OR = 1.42 95% CI 1.27, 1.59); red blood cell (RBC) count (OR = 2.52 95% CI 1.67, 3.78); PSA (OR = 1.04 95% CI 1.03, 1.05); and, creatinine (OR = 1.55 95% CI 1.12, 2.15). Comparing all PC stages versus non-cancerous conditions, the ROC curve area under the curve (AUC) enlarged (increasing the probability of correctly classifying PC): PSA (alone) 0.59 (95% CI 0.55, 0.61); PBCDR model 0.68 (95% CI 0.65, 0.71), and the positive predictive value (PPV) increased: PSA 44.7%; PBCDR model 61.8%. Comparing PC (stages II, III, IV) vs. other, the ROC AUC increased: PSA (alone) 0.63 (95% CI 0.58, 0.66); PBCDR model 0.72 (95% CI 0.68, 0.75), and the PPV increased: 20.6% (PSA); PBCDR model 55.3%. Conclusions These results suggest evaluating certain common biomarkers in conjunction with PSA may improve PC prediction prior to biopsy. Moreover, these biomarkers may be more helpful in detecting clinically relevant PC. Follow-up studies should begin with replicating the study on different U.S. VA patients involving multiple practices.</p

Improving Identification of Fall-Related Injuries in Ambulatory Care Using Statistical Text Mining

Objectives. We determined whether statistical text mining (STM) can identify fall-related injuries in electronic health record (EHR) documents and the impact on STM models of training on documents from a single or multiple facilities. Methods. We obtained fiscal year 2007 records for Veterans Health Administration (VHA) ambulatory care clinics in the southeastern United States and Puerto Rico, resulting in a total of 26 010 documents for 1652 veterans treated for fall-related injury and 1341 matched controls. We used the results of an STM model to predict fall-related injuries at the visit and patient levels and compared them with a reference standard based on chart review. Results. STM models based on training data from a single facility resulted in accuracy of 87.5% and 87.1%, F-measure of 87.0% and 90.9%, sensitivity of 92.1% and 94.1%, and specificity of 83.6% and 77.8% at the visit and patient levels, respectively. Results from training data from multiple facilities were almost identical. Conclusions. STM has the potential to improve identification of fall-related injuries in the VHA, providing a model for wider application in the evolving national EHR system

Quality of Care for Veterans with Chronic Diseases: Performance on Quality Indicators, Medication Use and Adherence, and Health Care Utilization

This study was conducted to assess and benchmark the quality of care, in terms of adherence to nationally recognized treatment guidelines, for veterans with common chronic diseases (ie, asthma, chronic obstructive pulmonary disease [COPD], coronary artery disease [CAD], diabetes, heart failure, hyperlipidemia [HL]) in a Veterans Health Administration (VHA) system. Patients with at least 1 of the target diagnoses in the period between January 2002 and mid-year 2006 were identified using electronic medical records of patients seen at the James A. Haley Veterans' Hospital in Tampa, Florida. The most common diseases identified were HL (34%), CAD (21%), and diabetes (19%). The percentage of patients filling a prescription for any guidelines-sanctioned pharmacotherapy ranged from 28% (heart failure) to 91% (asthma). Persistence to medication ranged from 21% (HL) to 63% (asthma), while compliance ranged from 49% (COPD) to 85% (CAD). Most patients with diabetes (88%) had at least 1 A1c test in a year, but only 47% of patients had A1c values <7%. This study found that quality of care was generally good for conditions such as cardiovascular disease and diabetes, but quality care for conditions that have not been a primary focus of previous VHA quality improvement efforts, such as asthma and COPD, has room for improvement. (Population Health Management 2011;14:99–106