An Administrative Claims Model for Profiling Hospital 30-Day Mortality Rates for Pneumonia Patients

Outcome measures for patients hospitalized with pneumonia may complement process measures in characterizing quality of care. We sought to develop and validate a hierarchical regression model using Medicare claims data that produces hospital-level, risk-standardized 30-day mortality rates useful for public reporting for patients hospitalized with pneumonia.Retrospective study of fee-for-service Medicare beneficiaries age 66 years and older with a principal discharge diagnosis of pneumonia. Candidate risk-adjustment variables included patient demographics, administrative diagnosis codes from the index hospitalization, and all inpatient and outpatient encounters from the year before admission. The model derivation cohort included 224,608 pneumonia cases admitted to 4,664 hospitals in 2000, and validation cohorts included cases from each of years 1998-2003. We compared model-derived state-level standardized mortality estimates with medical record-derived state-level standardized mortality estimates using data from the Medicare National Pneumonia Project on 50,858 patients hospitalized from 1998-2001. The final model included 31 variables and had an area under the Receiver Operating Characteristic curve of 0.72. In each administrative claims validation cohort, model fit was similar to the derivation cohort. The distribution of standardized mortality rates among hospitals ranged from 13.0% to 23.7%, with 25(th), 50(th), and 75(th) percentiles of 16.5%, 17.4%, and 18.3%, respectively. Comparing model-derived risk-standardized state mortality rates with medical record-derived estimates, the correlation coefficient was 0.86 (Standard Error = 0.032).An administrative claims-based model for profiling hospitals for pneumonia mortality performs consistently over several years and produces hospital estimates close to those using a medical record model

A systematic comparison of software dedicated to meta-analysis of causal studies

Background: Our objective was to systematically assess the differences in features, results, and usability of currently available meta-analysis programs. Methods: Systematic review of software. We did an extensive search on the internet (Google, Yahoo, Altavista, and MSN) for specialized meta-analysis software. We included six programs in our review: Comprehensive Meta-analysis (CMA), MetAnalysis, MetaWin, MIX, RevMan, and WEasyMA. Two investigators compared the features of the software and their results. Thirty independent researchers evaluated the programs on their usability while analyzing one data set. Results: The programs differed substantially in features, ease-of-use, and price. Although most results from the programs were identical, we did find some minor numerical inconsistencies. CMA and MIX scored highest on usability and these programs also have the most complete set of analytical features. Conclusion: In consideration of differences in numerical results, we believe the user community would benefit from openly available and systematically updated information about the procedures and results of each program's validation. The most suitable program for a meta-analysis will depend on the user's needs and preferences and this report provides an overview that should be helpful in making a substantiated choice

Multilevel latent class casemix modelling: a novel approach to accommodate patient casemix

<p>Abstract</p> <p>Background</p> <p>Using routinely collected patient data we explore the utility of multilevel latent class (MLLC) models to adjust for patient casemix and rank Trust performance. We contrast this with ranks derived from Trust standardised mortality ratios (SMRs).</p> <p>Methods</p> <p>Patients with colorectal cancer diagnosed between 1998 and 2004 and resident in Northern and Yorkshire regions were identified from the cancer registry database (n = 24,640). Patient age, sex, stage-at-diagnosis (Dukes), and Trust of diagnosis/treatment were extracted. Socioeconomic background was derived using the Townsend Index. Outcome was survival at 3 years after diagnosis. MLLC-modelled and SMR-generated Trust ranks were compared.</p> <p>Results</p> <p>Patients were assigned to two classes of similar size: one with reasonable prognosis (63.0% died within 3 years), and one with better prognosis (39.3% died within 3 years). In patient class one, all patients diagnosed at stage B or C died within 3 years; in patient class two, all patients diagnosed at stage A, B or C survived. Trusts were assigned two classes with 51.3% and 53.2% of patients respectively dying within 3 years. Differences in the ranked Trust performance between the MLLC model and SMRs were all within estimated 95% CIs.</p> <p>Conclusions</p> <p>A novel approach to casemix adjustment is illustrated, ranking Trust performance whilst facilitating the evaluation of factors associated with the patient journey (e.g. treatments) and factors associated with the processes of healthcare delivery (e.g. delays). Further research can demonstrate the value of modelling patient pathways and evaluating healthcare processes across provider institutions.</p

Can machine learning complement traditional medical device surveillance? A case-study of dual-chamber implantable cardioverter&ndash;defibrillators

Joseph S Ross,1&ndash;4 Jonathan Bates,4 Craig S Parzynski,4 Joseph G Akar,4,5 Jeptha P Curtis,4,5 Nihar R Desai,4,5 James V Freeman,4,5 Ginger M Gamble,4 Richard Kuntz,6 Shu-Xia Li,4 Danica Marinac-Dabic,7 Frederick A Masoudi,8 Sharon-Lise T Normand,9,10 Isuru Ranasinghe,11 Richard E Shaw,12 Harlan M Krumholz2&ndash;5 1Section of General Medicine, Department of Medicine, 2Robert Wood Johnson Foundation Clinical Scholars Program, Yale School of Medicine, 3Department of Health Policy and Management, Yale School of Public Health, 4Center for Outcomes Research and Evaluation, Yale&ndash;New Haven Hospital, 5Section of Cardiovascular Medicine, Department of Medicine, Yale School of Medicine, New Haven, CT, 6Medtronic Inc, Minneapolis, MN, 7Division of Epidemiology, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, 8Division of Cardiology, Department of Medicine, University of Colorado, Aurora, CO, 9Department of Health Care Policy, Harvard Medical School, 10Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA; 11Discipline of Medicine, University of Adelaide, Adelaide, SA, Australia; 12Department of Clinical Informatics, California Pacific Medical Center, San Francisco, CA, USA Background: Machine learning methods may complement traditional analytic methods for medical device surveillance.Methods and results: Using data from the National Cardiovascular Data Registry for implantable cardioverter&ndash;defibrillators (ICDs) linked to Medicare administrative claims for longitudinal follow-up, we applied three statistical approaches to safety-signal detection for commonly used dual-chamber ICDs that used two propensity score (PS) models: one specified by subject-matter experts (PS-SME), and the other one by machine learning-based selection (PS-ML). The first approach used PS-SME and cumulative incidence (time-to-event), the second approach used PS-SME and cumulative risk (Data Extraction and Longitudinal Trend Analysis [DELTA]), and the third approach used PS-ML and cumulative risk (embedded feature selection). Safety-signal surveillance was conducted for eleven dual-chamber ICD models implanted at least 2,000 times over 3 years. Between 2006 and 2010, there were 71,948 Medicare fee-for-service beneficiaries who received dual-chamber ICDs. Cumulative device-specific unadjusted 3-year event rates varied for three surveyed safety signals: death from any cause, 12.8%&ndash;20.9%; nonfatal ICD-related adverse events, 19.3%&ndash;26.3%; and death from any cause or nonfatal ICD-related adverse event, 27.1%&ndash;37.6%. Agreement among safety signals detected/not detected between the time-to-event and DELTA approaches was 90.9% (360 of 396, k=0.068), between the time-to-event and embedded feature-selection approaches was 91.7% (363 of 396, k=&ndash;0.028), and between the DELTA and embedded feature selection approaches was 88.1% (349 of 396, k=&ndash;0.042).Conclusion: Three statistical approaches, including one machine learning method, identified important safety signals, but without exact agreement. Ensemble methods may be needed to detect all safety signals for further evaluation during medical device surveillance. Keywords: implanted cardioverter&ndash;defibrillator, methodology, surveillanc

The Rippling effect of social influence via phone communication network

Phenomena such as “small-world” and “six degrees of separation” reveal the connectivity between individuals that are seemingly unrelated in the society. Beyond merely connectivity, it has been shown in recent years that social contagion exists in online interactions. Along this line of investigation, we are interested in the subtle and invisible social influence on real-world behavior across offline communication networks. In particular, we study how social influence propagates and triggers behavioral change, and how such effect expands deeply across the social network in a way similar to the physical phenomenon of ripples across the water. To this end, we analyze a large-scale one-month international event in Andorra using nation-wide mobile phone data, and investigate the change in the likelihood of attending the event for people that have been influenced by and are of different social distances from the attendees. Our results suggest that social influence exhibits the ripple effect, decaying across social distances from the source but persisting up to six degrees of separation in the social network. We further show that such influence decays as communication delay increases and communication intensity decreases, and that it is stronger among people who are more explorative geographically. Our findings may have important implications in a number of domains, such as marketing, public health, and social mobilizations