Die Harnconcretionen, ihre Entstehung, Erkennung und Analyse, mit besonderer Rücksicht auf Diagnose und Therapie der Nieren- und Blasenerkrankung.

Mode of access: Internet.

Die Pathologie und Diagnose der Krankheiten der Brust, ins Besondere erläutert durch eine rationelle Erklärung ihrer physikalischen Zeichen nebst neuen Untersuchung über die Tone des herzens.

Translation of The pathology and diagnosis of diseases of the chest.

How to diagnose smallpox : a guide for general practitioners, post-graduate students and others /

Mode of access: Internet.

Spezielle Diagnose der inneren Krankheiten : ein Handbuch für Ärzte und Studierende /

Includes index.

The blood; how to examine and diagnose its diseases.

Mode of access: Internet.

Ueber die thermische Tuberkulinreaktion bei vortuberkulinisierten Rindern sowie über die Anwendbarkeit der Ophthalmo- und Cutisreaktion zur Diagnose der Rindertuberkulose.

Inaug.-diss.--Leipzig, 1908.

Beitrag zur Diagnose des Infektiösen Abortus der Kühe mit Hilfe der Präzipitation ...

Lebenslauf.

Diagnose und therapie der inneren krankheiten des hundes, einschliesslich der haut-, aungen- und ohrenerkrankungen, sowie einiger chirurgischer leiden.

Mode of access: Internet.

Drift and mastery; an attempt to diagnose the current unrest,

Mode of access: Internet.

Die Diagnose der Blasen- und Nierenkrankheiten mittelst der Harnleiterpincette.

Bibliographical foot-notes.

Specielle diagnose der inneren Krankheiten. Ein Handbuch für Ärzte und Studirende,

Mode of access: Internet.

Noninvasive tests for arterial structure, function, and compliance: Do they identify risk or diagnose disease?

Background: Brachial artery reactivity (BAR), carotid intima-media thickness (IMT), and applanation tonometry for evaluation of total arterial compliance may provide information about preclinical vascular disease. We sought to determine whether these tests could be used to identify patients with coronary artery disease (CAD) without being influenced by their ability to identify those at risk ford CAD developing. Methods: We studied 100 patients and compared 3 groups: 35 patients with known CAD; 34 patients with symptoms and risk factors but no CAD identified by stress echocardiography (risk group); and 31 control subjects. BAR and IMT were measured using standard methods, and total arterial compliance was calculated by the pulse-pressure method from simultaneous radial applanation tonometry and pulsed wave Doppler of the left ventricular outflow. Ischemia was identified as a new or worsening wall-motion abnormality induced by stress. Results: In a comparison between the control subjects and patients either at risk for developing CAD or with CAD, the predictors of risk for CAD were: age (P = .01); smoking history (P = .002); hypercholesterolemia (P = .002); and hypertension (P = .004) (model R = 0.82; P = .0001). The independent predictors of CAD were: IMT (P = .001); BAR (P = .04); sex (P = .005); and hypertension (P = .005) (model R = 0.80; P = .0001). Conclusion: IMT, BAR, and traditional cardiovascular risk factors appear to identify patients at risk for CAD developing. However, only IMT was significantly different between patients at risk for developing CAD and those with overt CAD.

Australian and New Zealand practice in intensive care (ANZPIC II): Predictors of physician confidence to diagnose pneumonia and determine illness severity in ventilated patients

The manner in which elements of clinical history, physical examination and investigations influence subjectively assessed illness severity and outcome prediction is poorly understood. This study investigates the relationship between clinician and objectively assessed illness severity and the factors influencing clinician's diagnostic confidence and illness severity rating for ventilated patients with suspected pneumonia in the intensive care unit (ICU). A prospective study of fourteen ICUs included all ventilated admissions with a clinical diagnosis of pneumonia. Data collection included pneumonia type - community-acquired (CAP), hospital-acquired (HAP) and ventilator-associated (VAP), clinician determined illness severity (CDIS), diagnostic methods, clinical diagnostic confidence (CDC), microbiological isolates and antibiotic use. For 476 episodes of pneumonia (48% CAP, 24% HAP, 28% VAP), CDC was greatest for CAP (64% CAP, 50% HAP and 49% VAP, P < 0.01) or when pneumonia was considered life-threatening (84% high CDC, 13% medium CDC and 3% low CDC, P < 0.001). Life-threatening pneumonia was predicted by worsening gas exchange (OR 4.8, CI 95% 2.3-10.2, P < 0.001), clinical signs of consolidation (OR 2.0, CI 95% 1.2-3.2, P < 0.01) and the Sepsis-Related Organ Failure Assessment (SOFA) Score (OR 1.1, CI 95% 1.1-1.2, P < 0.001). Diagnostic confidence increased with CDIS (OR 163, CI 95% 8.4-31.4, P < 0.001), definite pathogen isolation (OR 3.3, CI 95% 2.0-5.6) and clinical signs of consolidation (OR 2.1, CI 95% 1.3-3.3, P = 0.001). Although the CDIS, SOFA Score and the Simplified Acute Physiologic Score (SAPS II) were all associated with mortality, the SAPS II Score was the best predictor of mortality (P = 0.02). Diagnostic confidence for pneumonia is moderate but increases with more classical presentations. A small set of clinical parameters influence subjective assessment. Objective assessment using SAPS II Scoring is a better predictor of mortality.

Host gene expression classifiers diagnose acute respiratory illness etiology.

Acute respiratory infections caused by bacterial or viral pathogens are among the most common reasons for seeking medical care. Despite improvements in pathogen-based diagnostics, most patients receive inappropriate antibiotics. Host response biomarkers offer an alternative diagnostic approach to direct antimicrobial use. This observational cohort study determined whether host gene expression patterns discriminate noninfectious from infectious illness and bacterial from viral causes of acute respiratory infection in the acute care setting. Peripheral whole blood gene expression from 273 subjects with community-onset acute respiratory infection (ARI) or noninfectious illness, as well as 44 healthy controls, was measured using microarrays. Sparse logistic regression was used to develop classifiers for bacterial ARI (71 probes), viral ARI (33 probes), or a noninfectious cause of illness (26 probes). Overall accuracy was 87% (238 of 273 concordant with clinical adjudication), which was more accurate than procalcitonin (78%, P < 0.03) and three published classifiers of bacterial versus viral infection (78 to 83%). The classifiers developed here externally validated in five publicly available data sets (AUC, 0.90 to 0.99). A sixth publicly available data set included 25 patients with co-identification of bacterial and viral pathogens. Applying the ARI classifiers defined four distinct groups: a host response to bacterial ARI, viral ARI, coinfection, and neither a bacterial nor a viral response. These findings create an opportunity to develop and use host gene expression classifiers as diagnostic platforms to combat inappropriate antibiotic use and emerging antibiotic resistance.