All-Cause Mortality and Progression Risks to Hepatic Decompensation and Hepatocellular Carcinoma in Patients Infected With Hepatitis C Virus.

BACKGROUND: A key question in care of patients with chronic hepatitis C virus (HCV) infection is beginning treatment immediately vs delaying treatment. Risks of mortality and disease progression in "real world" settings are important to assess the implications of delaying HCV treatment. METHODS: This was a cohort study of HCV patients identified from 4 integrated health systems in the United States who had liver biopsies during 2001-2012. The probabilities of death and progression to hepatocellular carcinoma, hepatic decompensation (hepatic encephalopathy, esophageal varices, ascites, or portal hypertension) or liver transplant were estimated over 1, 2, or 5 years by fibrosis stage (Metavir F0-F4) determined by biopsy at beginning of observation. RESULTS: Among 2799 HCV-monoinfected patients who had a qualifying liver biopsy, the mean age at the time of biopsy was 50.7 years. The majority were male (58.9%) and non-Hispanic white (66.9%). Over a mean observation of 5.0 years, 261 (9.3%) patients died and 34 (1.2%) received liver transplants. At 5 years after biopsy, the estimated risk of progression to hepatic decompensation or hepatocellular carcinoma was 37.2% in stage F4, 19.6% in F3, 4.7% in F2, and 2.3% in F0-F1 patients. Baseline biopsy stage F3 or F4 and platelet count below normal were the strongest predictors of progression to hepatic decompensation or hepatocellular carcinoma. CONCLUSIONS: The risks of death and progression to liver failure varied greatly by fibrosis stage. Clinicians and policy makers could use these progression risk data in prioritization and in determining the timing of treatment for patients in early stages of liver disease.

Prevalence of heart failure signs and symptoms in a large primary care population identified through the use of text and data mining of the electronic health record.

BACKGROUND: The electronic health record (EHR) contains a tremendous amount of data that if appropriately detected can lead to earlier identification of disease states such as heart failure (HF). Using a novel text and data analytic tool we explored the longitudinal EHR of over 50,000 primary care patients to identify the documentation of the signs and symptoms of HF in the years preceding its diagnosis. METHODS AND RESULTS: Retrospective analysis consisted of 4,644 incident HF cases and 45,981 group-matched control subjects. Documentation of Framingham HF signs and symptoms within encounter notes were carried out with the use of a previously validated natural language processing procedure. A total of 892,805 affirmed criteria were documented over an average observation period of 3.4 years. Among eventual HF cases, 85% had ≥1 criterion within 1 year before their HF diagnosis, as did 55% of control subjects. Substantial variability in the prevalence of individual signs and symptoms were found in both case and control subjects. CONCLUSIONS: HF signs and symptoms are frequently documented in a primary care population as identified through automated text and data mining of EHRs. Their frequent identification demonstrates the rich data available within EHRs that will allow for future work on automated criterion identification to help develop predictive models for HF.

Underreporting of Hepatitis B and C virus infections - Pennsylvania, 2001-2015.

CONTEXT: In Pennsylvania, reporting of viral hepatitis B (HBV) and viral hepatitis C (HCV) infections to CDC has been mandated since 2002. Underreporting of HBV and HCV infections has long been identified as a problem. Few reports have described the accuracy of state surveillance case registries for recording clinically-confirmed cases of HBV and HCV infections, or the characteristics of populations associated with lower rates of reporting. OBJECTIVE: The primary objective of the current study is to estimate the proportion of HBV and HCV infections that went unreported to the Pennsylvania Department of Health (PDoH), among patients in the Geisinger Health System of Pennsylvania. As a secondary objective, we study the association between underreporting of HBV and HCV infections to PDoH, and the select patient characteristics of interest: sex, age group, race/ethnicity, rural status, and year of initial diagnosis. DESIGN: Per medical record review, the study population was limited to Geisinger Health System patients, residing in Pennsylvania, who were diagnosed with a chronic HBV and/or HCV infection, between 2001 and 2015. Geisinger Health System patient medical records were matched to surveillance records of confirmed cases reported to the Pennsylvania Department of Health (PDoH). To quantify the extent that underreporting occurred among the Geisinger Health System study participants, we calculated the proportion of study participants that were not reported to PDoH as confirmed cases of HBV or HCV infections. An analysis of adjusted prevalence ratio estimates was conducted to study the association between underreporting of HBV and HCV infections to PDoH, and the select patient characteristics of interest. RESULTS: Geisinger Health System patients living with HBV were reported to PDoH 88.4% (152 of 172) of the time; patients living with HCV were reported to PDoH 94.6% (2,257 of 2,386) of the time; and patients who were co-infected with both viruses were reported to PDoH 72.0% (18 of 25) of the time. Patients living with HCV had an increased likelihood of being reported if they were: less than or equal to age 30 vs ages 65+ {PR = 1.2, [95%CI, (1.1, 1.3)]}, and if they received their initial diagnosis of HCV during the 2010-2015 time period vs the 1990-1999 time period {PR = 1.08, [95%CI, (1.05, 1.12)]}. CONCLUSION: The findings in this study are promising, and suggests that PDoH has largely been successful with tracking and monitoring viral hepatitis B and C infections, among persons that were tested for HBV and/or HCV. Additional efforts should be placed on decreasing underreporting rates of HCV infections among seniors (ages 65 and over), and persons who are co-infected with HBV and HCV.

Antipsychotic medications and extreme weight gain in two health systems.

OBJECTIVE: We conducted this study to investigate the rate of clinically important, extreme weight gain (EWG; ≥7% body weight gain) among all second generation antipsychotic (SGA) users in two large health care systems in the United States. STUDY DESIGN: Retrospective observational cohort study. METHODS: We used electronic medical record databases of two health systems to identify adults aged 18-79 years who from 1 January 2004 to 31 December 2011 had initiated a SGA medication. All patients had to have a minimum of two weight measures in the medical record: (1) one or more weights in the 180-day pre-treatment (baseline) period; and (2) one or more weights in the first year after initiating SGA treatment. RESULTS: We found that EWG occurred in 7.7-17.0% of SGA users. At one year, the average weight gain was nearly 10kg among SGA users who experienced EWG. Olanzapine was the SGA most commonly associated with EWG with a rate of 17.0 per 100 users [95% confidence interval (CI): 14.2-20.5], while ziprasidone was least commonly associated with EWG (7.7 per 100 users; 95% CI: 4.6-13.0). CONCLUSIONS: We found that clinically-important weight gain was common after the initiation of SGA treatment, and the EWG phenotype was easily identifiable within electronic medical records. There was significant heterogeneity in the rate of EWG across SGA medications. Weight gains of this magnitude are likely to have adverse health consequences and there is a significant unmet opportunity for physicians to identify these events and mitigate the harms of SGA use.

Early detection of heart failure with varying prediction windows by structured and unstructured data in electronic health records.

Heart failure (HF) prevalence is increasing and is among the most costly diseases to society. Early detection of HF would provide the means to test lifestyle and pharmacologic interventions that may slow disease progression and improve patient outcomes. This study used structured and unstructured data from electronic health records (EHR) to predict onset of HF with a particular focus on how prediction accuracy varied in relation to time before diagnosis. EHR data were extracted from a single health care system and used to identify incident HF among primary care patients who received care between 2001 and 2010. A total of 1,684 incident HF cases were identified and 13,525 controls were selected from the same primary care practices. Models were compared by varying the beginning of the prediction window from 60 to 720 days before HF diagnosis. As the prediction window decreased, the performance [AUC (95% CIs)] of the predictive HF models increased from 65% (63%-66%) to 74% (73%-75%) for the unstructured, from 73% (72%-75%) to 81% (80%-83%) for the structured, and from 76% (74%-77%) to 83% (77%-85%) for the combined data.

Combining knowledge and data driven insights for identifying risk factors using electronic health records.

BACKGROUND: The ability to identify the risk factors related to an adverse condition, e.g., heart failures (HF) diagnosis, is very important for improving care quality and reducing cost. Existing approaches for risk factor identification are either knowledge driven (from guidelines or literatures) or data driven (from observational data). No existing method provides a model to effectively combine expert knowledge with data driven insight for risk factor identification. METHODS: We present a systematic approach to enhance known knowledge-based risk factors with additional potential risk factors derived from data. The core of our approach is a sparse regression model with regularization terms that correspond to both knowledge and data driven risk factors. RESULTS: The approach is validated using a large dataset containing 4,644 heart failure cases and 45,981 controls. The outpatient electronic health records (EHRs) for these patients include diagnosis, medication, lab results from 2003-2010. We demonstrate that the proposed method can identify complementary risk factors that are not in the existing known factors and can better predict the onset of HF. We quantitatively compare different sets of risk factors in the context of predicting onset of HF using the performance metric, the Area Under the ROC Curve (AUC). The combined risk factors between knowledge and data significantly outperform knowledge-based risk factors alone. Furthermore, those additional risk factors are confirmed to be clinically meaningful by a cardiologist. CONCLUSION: We present a systematic framework for combining knowledge and data driven insights for risk factor identification. We demonstrate the power of this framework in the context of predicting onset of HF, where our approach can successfully identify intuitive and predictive risk factors beyond a set of known HF risk factors.

Patient satisfaction and physician productivity: complementary or mutually exclusive?

Motivating physicians to increase productivity and maximize patient satisfaction may result in conflicted behavior, raising questions about whether one must be sacrificed for the other. To determine if high satisfaction (measured by Press Ganey patient satisfaction survey) can be achieved while maintaining high productivity (measured in McGladrey relative value units, MRVU), longitudinal data collected from January 2002 to July 2004 were modeled using repeated measures regression. A total of 136 000 patient-completed satisfaction questionnaires evaluating 417 physicians were collected for analysis. Patient confidence (positively correlated; P = .001) and physician/patient time (inversely correlated; P = .001) were associated with higher physician productivity. Increases in MRVU were associated with decreases in patient perceptions of time with the physician (P = .003). The relationships between patient satisfaction and physician productivity were relatively small, suggesting that they are not necessarily incompatible and that both can be improved simultaneously.