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BACKGROUND: The pattern recognition molecule pentraxin-3 (PTX3) is a novel potential marker of prognosis, as elevated levels are associated with both disease severity and mortality in patients with a wide range of conditions. However, the usefulness of PTX3 as a prognostic biomarker in a general hospital setting is unknown.PATIENTS AND METHODS: The study cohort consisted of 1326 unselected, consecutive patients (age >40 years) admitted to a community hospital in Copenhagen, Denmark. Patients were followed until death or for a median of 11.5 years after admission. The main outcome measure was all-cause mortality. Serum samples collected from patients at admission and from 192 healthy control subjects were quantified for PTX3 level by enzyme-linked immunosorbent assay.RESULTS: PTX3 was elevated in patients (median 3.7 ng mL(-1) , range 0.5-209.8) compared with healthy nonhospitalized subjects (median 3.5 ng mL(-1) , range 0.0-8.3; P = 0.0003). Elevated PTX3 levels, defined as above the 95th percentile of the concentration in healthy subjects, were associated with increased overall mortality during the study (P < 0.0001). This increase in mortality was greatest in the short term, with an unadjusted hazard ratio (HR) of 6.4 [95% confidence interval (CI) 3.8-11.0] at 28 days after admission, compared to 1.7 (95% CI 1.4-2.0) at the end of follow-up. These results were still significant after adjustment for age, gender and glomerular filtration rate: adjusted HR of 5.0 (95% CI 2.9-8.8) and 1.4 (95% CI 1.2-1.8), respectively.CONCLUSION: These results suggest that PTX3 could be a widely applicable marker of short-term mortality in hospitalized patients and may be useful in the initial risk stratification.

Genomic Grade Index (GGI) is a 97-gene signature that improves histologic grade (HG) classification in invasive breast carcinoma. In this prospective study we sought to evaluate the feasibility of performing GGI in routine clinical practice and its impact on treatment recommendations., Journal Article, Research Support, Non-U.S. Gov't, SCOPUS: ar.j, info:eu-repo/semantics/published

Genomic Grade Index (GGI) is a 97-gene signature that improves histologic grade (HG) classification in invasive breast carcinoma. In this prospective study we sought to evaluate the feasibility of performing GGI in routine clinical practice and its impact on treatment recommendations., Journal Article, Research Support, Non-U.S. Gov't, SCOPUS: ar.j, info:eu-repo/semantics/published

Background: Many hospital rankings rely on the frequency of adverse outcomes and are based on administrative data. In the study presented here, we tried to find out, to what extent available administrative data of German Sickness Funds allow for an adequate hospital ranking and compared this with rankings based on additional information derived from a patient survey. Total hip replacement was chosen as an example procedure. In part I of the publication, we present the results of the approach based on administrative data.Methods: We used administrative data from the AOK-Lower Saxony of the years 2000, 2001 and 2002. The study population comprised all beneficiaries, who received total hip replacement in the years 2000 or 2001. Performance indicators used where "critical incident (Mortality or revision)" and "number of revisions" within the first year. Hospitals were ranked if they performed at least 20 procedures on AOK-beneficiaries in each of the two years. Multivariate modelling (logistic and poisson regression) was used to estimate the performance indicators by case-mix variables (age, sex, co-diagnoses) and hospital characteristics (hospital size, surgical volume). The actual ranking was based on these multivariate models, excluding hospital variables and adding dummy-variables for each hospital. Hospitals were ranked by their case-mix adjusted odds ratio or SMR respectively with respect to a pre-selected reference hospital. The resulting rankings were compared with each other, with regard to temporal stability, and the impact of case-mix variables.Results: About 4500 beneficiaries received total hip replacement in each year (n2000: 4482; n2001: 4579). The ranking included 65 hospitals. Comparing the years 2000 and 2001, the temporal stability of the rankings based on a single performance indicator was low (Spearman rang correlation coefficients 0.158 and 0.191). The agreement of rankings based on different performance indicators in the same year was high (Spearm, Hintergrund: Krankenhausrankings beziehen sich häufig auf schwere, unerwünschte Therapieresultate und beruhen auf routinemäßig verfügbaren Abrechnungsdaten. Im vorliegenden Projekt wurde am Beispiel der Implantation künstlicher Hüftgelenke untersucht, ob und inwieweit die den gesetzlichen Krankenkassen zur Verfügung stehenden Routinedaten für vergleichende Qualitätsbewertungen auf der Ebene einzelner Krankenhäuser bereits ausreichend sind oder ob eine inhaltlich angemessene Bewertung erst nach Ergänzung der Routinedaten um zusätzlich erhobene patientenseitige Informationen möglich ist. Im ersten Teil der Publikation werden die Ergebnisse des allein auf Routinedaten basierenden Qualitätsvergleichs vorgestellt.Methodik: Zur Verfügung standen Routinedaten der AOK Niedersachsen der Jahre 2000, 2001 und 2002 (u.a. Stammdaten und Daten nach SGB V § 301). Die Studienpopulation umfasste alle Versicherten, die im Jahr 2000 oder 2001 ein künstliches Hüftgelenk erhielten. Das Krankenhaus-Ranking erfolgte anhand der Ergebnisindikatoren "Kritisches Ereignis (Mortalität oder Revision)" und "Anzahl der Revisionsoperationen" für Krankenhäuser, die in jedem der beiden Jahre mindestens 20 Fälle hatten. Im ersten Schritt erfolgte eine multivariate Modellierung (logistische bzw. Poisson-Regression) der Ergebnisindikatoren unter Berücksichtigung der Einflussgrößen Alter, Geschlecht, Nebendiagnosen, Krankenhausgröße und Operationsvolumen. Für das eigentliche Ranking wurden diese Modelle ohne Krankenhausmerkmale, aber ergänzt um Indikatorvariablen der einzelnen Krankenhäuser, erneut berechnet. Maßgeblich für den Rangplatz war das hinsichtlich der Einflussgrößen adjustierte Odds Ratio bzw. SMR des einzelnen Krankenhauses in Bezug auf ein vorher festgelegtes Referenzkrankenhaus. Die verschiedenen Ranglisten wurden untereinander verglichen und in Bezug auf die zeitliche Stabilität sowie den Einfluss der Fallmix-Variablen untersucht.Ergebnisse: In jedem der beiden Beobachtungsjahre erhielten

Background: Many hospital rankings rely on the frequency of adverse outcomes and are based on administrative data. In the study presented here, we tried to find out, to what extent available administrative data of German Sickness Funds allow for an adequate hospital ranking and compared this with rankings based on additional information derived from a patient survey. Total hip replacement was chosen as an example procedure. In part II of the publication, we present the results of the approach based on administrative and patient-derived data.Methods: We used administrative data from a large health insurance (AOK-Lower Saxony) of the year 2002 and from a patient survey. The study population comprised mainly beneficiaries, who received primary total hip replacement in the year 2002, were mailed a survey 6 month post-operatively and participated in the survey. Performance indicators used where "Revision", "Complications" and "Change of functional impairment". Hospitals were ranked if they performed at least 20 procedures on AOK-beneficiaries. Multivariate modelling (logistic regression and generalized linear models) was used to estimate the performance indicators by case-mix variables (a.o. age, sex, co-morbidity, medical history) and hospital characteristics (hospital size, surgical volume). The actual ranking was based on these multivariate models, excluding hospital variables and adding dummy-variables for each hospital. Hospitals were ranked by their case-mix adjusted odds ratio or Standardized Difference (SDR) with respect to a pre-selected reference hospital. The resulting rankings were compared with each other and with regard to the impact of case-mix variables.Results: 4089 beneficiaries received primary total hip replacement in 2002. 3293 patients participated in the survey (80.5%). The ranking included 60 hospitals. The agreement of rankings based on different performance indicators in the same year was low to high (a correlation coefficient of Spearman betwee, Hintergrund: Krankenhausrankings beziehen sich häufig auf schwere, unerwünschte Therapieresultate und beruhen auf routinemäßig verfügbaren Abrechnungsdaten. Im vorliegenden Projekt wurde am Beispiel der Implantation künstlicher Hüftgelenke untersucht, ob und inwieweit die den gesetzlichen Krankenkassen zur Verfügung stehenden Routinedaten für vergleichende Qualitätsbewertungen auf der Ebene einzelner Krankenhäuser bereits ausreichend sind oder ob eine inhaltlich angemessene Bewertung erst nach Ergänzung der Routinedaten um zusätzlich erhobene patientenseitige Informationen möglich ist. Im zweiten Teil der Publikation werden die Ergebnisse des auf der Kombination von Routinedaten und Patientenbefragung basierenden Qualitätsvergleichs vorgestellt. Methodik: Zur Verfügung standen Routinedaten der AOK Niedersachsen des Jahres 2002 (u.a. Stammdaten und Daten nach SGB V § 301) sowie Daten einer Versichertenbefragung. Die Studienpopulation umfasste hauptsächlich Versicherten, die im Jahr 2002 erstmals ein künstliches Hüftgelenk erhielten, sechs Monate postoperativ angeschrieben wurden und an der Befragung teilnahmen. Das Krankenhaus-Ranking erfolgte anhand der Ergebnisindikatoren "Revision", "selbstberichtete Komplikationen" und "Veränderungen in der Alltagsfunktionalität" für Krankenhäuser, die im Jahr 2002 mindestens 20 Fälle hatten. Im ersten Schritt erfolgte eine multivariate Modellierung (logistische Regression bzw. Generalisiertes lineares Modell) der Ergebnisindikatoren unter Berücksichtigung verschiedener Einflussgrößen (u.a. Alter, Geschlecht, Begleiterkrankungen, Anmanese, präoperative Alltagsfunktion, Operationsvolumen). Für das eigentliche Ranking wurden diese Modelle ohne Krankenhausmerkmale, aber ergänzt um Indikatorvariablen der einzelnen Krankenhäuser, erneut berechnet. Maßgeblich für den Rangplatz war das hinsichtlich der patientenbezogenen Einflussgrößen adjustierte Odds Ratio (Ergebnisindikatoren "Revision" und "Komplikationen") bzw. die Standardisierte

Photo of entrance of Hahnemann Hospital administration building. Ambulance parked in front, snow on the ground. Handwritten text reads, "The morning after the hardest snow storm this winter 15-16." "Office Entrance"

The hospital library. Two nurses wearing the nursing cap volunteering in the library, one seated working at desk, and one standing sorting books while a visitor reads at the table. The library had a small window and three main book cases.

Hahnemann Hospital ambulance with driver parked in front of the garage. "Hahnemann Hospital" on side of the ambulance.

Close up photo of the side of a hospital ambulance circa. 1920's. Window in the cap of the ambulance, two female nurses sitting in the back of the ambulance looking out the window.