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The United States Food and Drug Administration (FDA) places considerable importance on the validation of software contained in and used to design, develop, or manufacture medical devices. This article discusses a recently published final FDA guidance document on software validation and the reasons for its importance.
United States (US) Food and Drug Administration (FDA) investigators have, for many years, consistently evaluated compliance with US process validation requirements. Until now there has been significant variability in the manner in which they have examined compliance with US requirements for the validation of software used in the production process or as part of the quality system. This article will discuss the contents of a recent FDA draft guidance document that may reduce this variability.
A Draft Guidance Document (Version 1.1) was issued by the United States Food and Drug Administration (FDA) to address the software validation requirement of the Quality System Regulation, 21 CFR Part 820, effective June 1, 1997. The guidance document outlines validation considerations that the FDA regards as applicable to both medical device software and software used to "design, develop or manufacture" medical devices. The Draft Guidance is available at the FDA web site http:@www.fda.gov/cdrh/comps/swareval++ +.html. Presented here is a review of the main features of the FDA document for Quality System Regulation (QSR), and some guidance for its implementation in industry.
We present a set of utilities and graphical user interface (GUI) tools for evaluating the quality of protein resonance assignments. The Assignment Validation Software (AVS) suite, together with new GUI features in the AutoAssign software package, provides a set of reports and graphs for validating protein resonance assignment data before its use in structure analysis and/or submission to the BioMagResBank (BMRB). Input includes a listing of resonance assignments and a summary of sequential connectivity data (i.e. triple resonance, NOE, or other data) used in deriving the assignments. These tools are useful for evaluating the accuracy of protein resonance assignments determined by either automated or manual methods.
The spot images from DNA microarray highly affect the discovery of biological knowledge from gene expression data. However, results from quality analysis, normalization, differential expression, and cluster analysis are rarely validated with spot images in current data analysis methods or software packages. We designed RealSpot, a software package, to validate the results by directly associating spot quality and data with spot images in a spreadsheet table. RealSpot splits hybridization images into individual spots stored in a spreadsheet table. It subsequently associates microarray data with spot images and performs data validation through the standard table operation such as sorting, searching, and editing. RealSpot has several built-in functions to facilitate data validation, including spot quality analysis, data organization, one-way ANOVA, gene ontology association, verification, import, and export. We used RealSpot to evaluate 77 slides (30,000 features each) from real hybridization experiments and to validate results from each step of data analysis. It took approximately 10 min to validate results of spot quality after initial evaluation and correct approximately 0.3% of falsely assigned qualities of 10,000 spots. We validated 1,641 of 2,110 differentially expressed genes identified by SAM analysis in approximately 1/2 h by comparing each gene with its respective spot image. Furthermore, we found that 6 of 48 genes in one cluster from k-mean clustering method showed inconsistent trends of spot images. RealSpot is efficient for validating microarray results and thus helpful for improving the reliability of the whole microarray experiment for experimentalists.
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The process of assessing the prediction ability of a computational model is called model validation. For models predicting a categorical response, the prediction ability is usually quantified by prediction measures such as sensitivity, specificity, and accuracy. This paper presents a software Model Validation using Repeated Partitioning (MVREP) that implements a computer-intensive, nonparametric approach to model validation, which we call the re-partitioning method. MVREP, developed using the SAS Macro language, repeats the process of randomly partitioning a dataset and subsequently performing standard model validation procedures, such as cross-validation, a large number of times and generates the empirical sampling distributions of prediction measures. The means of the sampling distributions serve as the point estimates of prediction measures of the model. The variances of the sampling distributions provide a direct assessment of variability for the point estimates of prediction measures. An example is presented using a mouse developmental toxicity chemical dataset to illustrate how the software can be used for the assessment of structure-activity relationships models.
This article examines the requirements of EN 60601-1-4, which states that risk management is an integral part of the development of software for medical devices. Food and Drug Administration regulations are also compared to the requirements of the Medical Device Directive to provide guidance on developing software for the European and US markets.
In the second part of this two-part article, the author reviews existing standards and current international and European standardization work, which serves to highlight the need for harmonized standards for medical devices that incorporate software. The new concepts of essential performance, and safety integrity, severity, and risk levels are examined, and these present a new basis for the revision of safety standards to make them relevant to the EC Directives.
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The Monte Carlo computational system for stochastic modelling of dietary exposure to food chemicals and nutrients is presented. This system was developed through a European Commission-funded research project. It is accessible as a Web-based application service. The system allows and supports very significant complexity in the data sets used as the model input, but provides a simple, general purpose, linear kernel for model evaluation. Specific features of the system include the ability to enter (arbitrarily) complex mathematical or probabilistic expressions at each and every input data field, automatic bootstrapping on subjects and on subject food intake diaries, and custom kernels to apply brand information such as market share and loyalty to the calculation of food and chemical intake.
Validation of standardized nursing language for use by specialty nurses is important for the design of computer software. The purposes of this study were to validate the usefulness of the 433 interventions in the Nursing Intervention Classification (NIC) for school nurses and to identify interventions that could be omitted from computer software for school nurses. A school nursing listserv, SCHLRN-L, was used to recruit volunteers. Ninety-three volunteers from the listserv also recruited 26 school nurses who were not members of the listserv. The total sample was 102 school nurses from 25 states and other areas, 76 listserv volunteers, and 26 others. E-mail was used to send and receive the survey forms to portions of the sample. A majority of interventions (n = 241; 56%) were selected as used by more than 50% of the sample. Of these, 53 direct care interventions were selected as used by more than 80% of the sample. Fifty interventions were not used by 100% of the sample. E-mail was a useful means to obtain a national sample and collect data.
INTRODUCTION: Patient Data Management Systems (PDMS) for ICUs collect, present and store clinical data. Various intentions make analysis of those digitally stored data desirable, such as quality control or scientific purposes. The aim of the Intensive Care Data Evaluation project (ICDEV), was to provide a database tool for the analysis of data recorded at various ICUs at the University Clinics of Vienna. SETTINGS: General Hospital of Vienna, with two different PDMSs used: CareVue 9000 (Hewlett Packard, Andover, USA) at two ICUs (one medical ICU and one neonatal ICU) and PICIS Chart+ (PICIS, Paris, France) at one Cardiothoracic ICU. CONCEPT AND METHODS: Clinically oriented analysis of the data collected in a PDMS at an ICU was the beginning of the development. After defining the database structure we established a client-server based database system under Microsoft Windows NI and developed a user friendly data quering application using Microsoft Visual C++ and Visual Basic; RESULTS: ICDEV was successfully installed at three different ICUs, adjustment to the different PDMS configurations were done within a few days. The database structure developed by us enables a powerful query concept representing an 'EXPERT QUESTION COMPILER' which may help to answer almost any clinical questions. Several program modules facilitate queries at the patient, group and unit level. Results from ICDEV-queries are automatically transferred to Microsoft Excel for display (in form of configurable tables and graphs) and further processing. CONCLUSIONS: The ICDEV concept is configurable for adjustment to different intensive care information systems and can be used to support computerized quality control. However, as long as there exists no sufficient artifact recognition or data validation software for automatically recorded patient data, the reliability of these data and their usage for computer assisted quality control remain unclear and should be further studied.
There is increasing interest in being able to automatically register medical images from either the same or different modalities. Registered images are proving useful in a range of applications, not only providing more correlative information to aid in diagnosis, but also assisting with the planning and monitoring of therapy, both surgery and radiotherapy. The practising nuclear medicine specialist is faced with a dilemma in choosing an appropriate method since the literature in the field is extensive, with conflicting evidence as to what methods are optimal. Although most barriers to implementing registration in routine practice have been removed, there remains a lack of commercial, validated software. The alternative is to install a dual-modality instrument. The objective of this review is to present a general overview of medical image registration with emphasis on the application and issues relevant to nuclear medicine.
Magnetic resonance (MR) imaging--guided prostate biopsy in a 0.5-T open imager is described, validated in phantom studies, and performed in two patients. The needles are guided by using fast gradient-recalled echo and T2-weighted fast spin-echo images. Surgical navigation software provided T2-weighted images critical to targeting the peripheral zone and the tumor. MR imaging can be used to guide prostate biopsy.
Structural genomics projects are providing large quantities of new 3D structural data for proteins. To monitor the quality of these data, we have developed the protein structure validation software suite (PSVS), for assessment of protein structures generated by NMR or X-ray crystallographic methods. PSVS is broadly applicable for structure quality assessment in structural biology projects. The software integrates under a single interface analyses from several widely-used structure quality evaluation tools, including PROCHECK (Laskowski et al., J Appl Crystallog 1993;26:283-291), MolProbity (Lovell et al., Proteins 2003;50:437-450), Verify3D (Luthy et al., Nature 1992;356:83-85), ProsaII (Sippl, Proteins 1993;17: 355-362), the PDB validation software, and various structure-validation tools developed in our own laboratory. PSVS provides standard constraint analyses, statistics on goodness-of-fit between structures and experimental data, and knowledge-based structure quality scores in standardized format suitable for database integration. The analysis provides both global and site-specific measures of protein structure quality. Global quality measures are reported as Z scores, based on calibration with a set of high-resolution X-ray crystal structures. PSVS is particularly useful in assessing protein structures determined by NMR methods, but is also valuable for assessing X-ray crystal structures or homology models. Using these tools, we assessed protein structures generated by the Northeast Structural Genomics Consortium and other international structural genomics projects, over a 5-year period. Protein structures produced from structural genomics projects exhibit quality score distributions similar to those of structures produced in traditional structural biology projects during the same time period. However, while some NMR structures have structure quality scores similar to those seen in higher-resolution X-ray crystal structures, the majority of NMR structures have lower scores. Potential reasons for this "structure quality score gap" between NMR and X-ray crystal structures are discussed.
A method is presented to refine models built by homology by the use of restricted molecular dynamics (MD) techniques. The basic idea behind this method is the use of structure validation software to determine for each residue the likelihood that it is modeled correctly. This information is used to determine constraints and restraints in an MD simulation including explicit solvent molecules, which is used for model refinement. The procedure is based on the idea that residues that the validation software identifies as correctly positioned should be strongly constrained or restrained in the MD simulations, whereas residues that are likely to be positioned wrongly should move freely. Two different protocols are compared: one (applied to CASP3 target T58) using full structural constraints with separate optimization of each short fragment and the other (applied to T47) allowing some freedom using harmonic restraining potentials, with automatic optimization of the whole molecule. Structures along the MD trajectory that scored best in structural checks were selected for the construction of models that appeared to be successful in the CASP3 competition. Model refinement with MD in general leads to a model that is less like the experimental structure (Levitt et al. Nature Struct Biol 1999;6:108-111). Actually, refined T47 was slightly improved compared to the starting model; changes in model T58 led not to further enhancement. After the X-ray structure of the modeled proteins became known, the procedure was evaluated for two targets (T47 and the CASP4 target T111) by comparing a long simulation in water with the experimental target structures. It was found that structural improvements could be obtained on a nanosecond time scale by allowing appropriate freedom in the simulation. Structural checks applied to fast fluctuations do not appear to be informative for the correctness of the structure. However, both a simple hydrogen bond count and a simple compactness measure, if averaged over times of typically 300 ps, correlate well with structural correctness and we suggest that criteria based on these properties may be used in computational folding strategies.