Cost-effectiveness of a multidimensional post-discharge disease management program for heart failure patients-economic evaluation along a one-year observation period.

OBJECTIVE: This study aimed to assess the cost-effectiveness of the telemedically assisted post-discharge management program (DMP) HerzMobil Tirol (HMT) for heart failure (HF) patients in clinical practice in Austria. METHODS: We conducted a cost-effectiveness analysis along a retrospective cohort study (2016-2019) of HMT with a propensity score matched cohort of 251 individuals in the HMT and 257 in the usual care (UC) group and a 1-year follow-up. We calculated the effectiveness (hospital-free survival, hospital-free life-years gained, and number of avoided rehospitalizations), costs (HMT, rehospitalizations), and the incremental cost-effectiveness ratio (ICER). We performed a nonparametric sensitivity analysis with bootstrap sampling and sensitivity analyses on costs of HF rehospitalizations and on costs per disease-related diagnosis (DRG) score for rehospitalizations. RESULTS: Base-case analysis showed that HMT resulted in an average of 42 additional hospital-free days, 40 additional days alive, and 0.12 avoided hospitalizations per patient-year compared with UC during follow-up. The average HMT costs were EUR 1916 per person. Mean rehospitalization costs were EUR 5551 in HMT and EUR 6943 in UC. The ICER of HMT compared to UC was EUR 4773 per life-year gained outside the hospital. In a sensitivity analysis, HMT was cost-saving when "non-HF related costs" related to the DMP were replaced with average costs. CONCLUSIONS: The economic evaluation along the cohort study showed that the HerzMobil Tirol is very cost-effective compared to UC and cost-saving in a sensitivity analysis correcting for "non-HF related costs." These findings promote a widespread adoption of telemedicine-assisted DMP for HF.

Feasibility and effectiveness of a multidimensional post-discharge disease management programme for heart failure patients in clinical practice: the HerzMobil Tirol programme.

AIMS: It remains unclear whether transitional care management outside of a clinical trial setting provides benefits for patients with acute heart failure (AHF) after hospitalization. We evaluated the feasibility and effectiveness of a multidimensional post-discharge disease management programme using a telemedical monitoring system incorporated in a comprehensive network of heart failure nurses, resident physicians, and secondary and tertiary referral centres (HerzMobil Tirol, HMT), METHODS AND RESULTS: The non-randomized study included 508 AHF patients that were managed in HMT (n = 251) or contemporaneously in usual care (UC, n = 257) after discharge from hospital from 2016 to 2019. Groups were retrospectively matched for age and sex. The primary endpoint was time to HF readmission and all-cause mortality within 6 months. Multivariable Cox proportional hazard models were used to assess the effectiveness. The primary endpoint occurred in 48 patients (19.1%) in HMT and 89 (34.6%) in UC. Compared with UC, management by HMT was associated with a 46%-reduction in the primary endpoint (adjusted HR 0.54; 95% CI 0.37-0.77; P < 0.001). Subgroup analyses revealed consistent effectiveness. The composite of recurrent HF hospitalization and death within 6 months per 100 patient-years was 64.2 in HMT and 108.2 in UC (adjusted HR 0.41; 95% CI 0.29-0.55; P < 0.001 with death considered as a competing risk). After 1 year, 25 (10%) patients died in HMT compared with 66 (25.7%) in UC (HR 0.38; 95% CI 0.23-0.61, P < 0.001). CONCLUSIONS: A multidimensional post-discharge disease management programme, comprising a telemedical monitoring system incorporated in a comprehensive network of specialized heart failure nurses and resident physicians, is feasible and effective in clinical practice.

Standardization of Pneumococcal Biofilm Release to PncO Expression, a Predictive Measurement of Virulence.

A critical component in clinical trials for vaccines against pneumococcal disease is the establishment of robust preclinical models and clinical correlates of protection, which, in the case of the causative bacterial agent Streptococcus pneumoniae, include standard sepsis/pneumonia mouse models and opsonophagocytic activity (OPA), respectively. Despite broad usage, these gold-standard measures are ill equipped to evaluate nontraditional antigens that target virulence factors beyond capsular polysaccharides and/or proteins not associated with colonization or routine growth. These assays are further complicated by observed inconsistencies in the expression of target protein antigens and in the quantity of usable bacteria provided from respective growth processes. In an effort to overcome these issues, we performed an extensive optimization study of the critical steps in a bacterial biofilm dispersion model (termed "the biofilm model") to identify conditions that yield the greatest quantity of released pneumococci displaying a consistent virulence phenotype. Using this knowledge, we developed a secondary immune absorbance assay to provide immediate insight into the phenotypic state of bacteria conditioned using the biofilm model. Specifically, positive correlations between the expression of PncO (a key virulence-associated protein antigen) and immune absorbance (R2 = 0.96), capsule shedding, and OPA assay titers were translated into a predictive readout of virulence in sepsis and pneumonia challenge models. These results present a methodology for generating consistent lots of virulent bacteria to standardize inputs in preclinical and clinical models for testing vaccines against biofilm-associated bacteria.

Time-series lipidomic analysis of the oleaginous green microalga species Ettlia oleoabundans under nutrient stress.

BACKGROUND: Microalgae are uniquely advantageous organisms cultured and harvested for several value-added biochemicals. A majority of these compounds are lipid-based, such as triacylglycerols (TAGs), which can be used for biofuel production, and their accumulation is most affected under nutrient stress conditions. As such, the balance between cellular homeostasis and lipid metabolism becomes more intricate to achieve efficiency in bioproduct synthesis. Lipidomics studies in microalgae are of great importance as biochemical diversity also plays a major role in lipid regulation among oleaginous species. METHODS: The aim of this study was to analyze time-series changes in lipid families produced by microalga under different nutrient conditions and growth phases to gain comprehensive information at the cellular level. For this purpose, we worked with a highly adaptable, oleaginous, non-model green microalga species, Ettlia oleoabundans (a.k.a. Neochloris oleoabundans). Using a mass spectrometry-based untargeted and targeted metabolomics' approach, we analyzed the changes in major lipid families under both replete and deplete nitrogen and phosphorus conditions at four different time points covering exponential and stationary growth phases. RESULTS: Comprehensive analysis of the lipid metabolism highlighted the accumulation of TAGs, which can be utilized for the production of biodiesel via transesterification, and depletion of chlorophylls and certain structural lipids required for photosynthesis, under nutrient deprived conditions. We also found a correlation between the depletion of digalactosyldiacylglycerols (DGDGs) and sulfoquinovosyldiacylglycerols (SQDGs) under nutrient deprivation. CONCLUSIONS: High accumulation of TAGs under nutrient limitation as well as a depletion of other lipids of interest such as phosphatidylglycerols (PGs), DGDGs, SQDGs, and chlorophylls seem to be interconnected and related to the microalgal photosynthetic efficiency. Overall, our results provided key biochemical information on the lipid regulation and physiology of a non-model green microalga, along with optimization potential for biodiesel and other value-added product synthesis.

LC-MS/MS quantification of short-chain acyl-CoA's in Escherichia coli demonstrates versatile propionyl-CoA synthetase substrate specificity.

AIMS: This paper utilized quantitative LC-MS/MS to profile the short-chain acyl-CoA levels of several strains of Escherichia coli engineered for heterologous polyketide production. To further compare and potentially expand the levels of available acyl-CoA molecules, a propionyl-CoA synthetase gene from Ralstonia solanacearum (prpE-RS) was synthesized and expressed in the engineered strain BAP1. METHODS AND RESULTS: Upon feeding propionate, the engineered E. coli strains had increased the levels of both propionyl- and methylmalonyl-CoA of 6- to 30-fold and 3·7- to 6·8-fold, respectively. Expression of prpE-RS resulted in no significant increases in acetyl-, butyryl- and propionyl-CoA when fed the corresponding substrates (sodium acetate, butyrate or propionate). More interesting, however, were the results from strain BAP1 engineered for native prpE overexpression, which indicated increases in the same range of acyl-CoA formation. CONCLUSIONS: The increased acyl-CoA levels across the strains profiled in this study reflect the genetic modifications implemented for improved polyketide production and also indicate flexibility of the native PrpE. SIGNIFICANCE AND IMPACT OF THE STUDY: The results provide direct evidence of enhanced acyl-CoA levels correlating to those strains engineered for polyketide biosynthesis. This information and the inherent flexibility of the native PrpE enzyme support future efforts to characterize, engineer and extend acyl-CoA precursor supply for additional heterologous biosynthetic attempts.

Construction and performance of heterologous polyketide-producing K-12- and B-derived Escherichia coli.

AIMS: Escherichia coli has emerged as a viable heterologous host for the production of complex, polyketide natural compounds. In this study, polyketide biosynthesis was compared between different E. coli strains for the purpose of better understanding and improving heterologous production. METHODS AND RESULTS: Both B and K-12 E. coli strains were genetically modified to support heterologous polyketide biosynthesis [specifically, 6-deoxyerythronolide B (6dEB)]. Polyketide production was analysed using a helper plasmid designed to overcome rare codon usage within E. coli. Each strain was analysed for recombinant protein production, precursor consumption, by-product production, and 6dEB biosynthesis. Of the strains tested for biosynthesis, 6dEB production was greatest for E. coli B strains. When comparing biosynthetic improvements as a function of mRNA stability vs codon bias, increased 6dEB titres were observed when additional rare codon tRNA molecules were provided. CONCLUSIONS: Escherichia coli B strains and the use of tRNA supplementation led to improved 6dEB polyketide titres. SIGNIFICANCE AND IMPACT OF THE STUDY: Given the medicinal potential and growing field of polyketide heterologous biosynthesis, the current study provides insight into host-specific genetic backgrounds and gene expression parameters aiding polyketide production through E. coli.

An epidemiological modeling and data integration framework.

OBJECTIVES: In this work, a cellular automaton software package for simulating different infectious diseases, storing the simulation results in a data warehouse system and analyzing the obtained results to generate prediction models as well as contingency plans, is proposed. The Brisbane H3N2 flu virus, which has been spreading during the winter season 2009, was used for simulation in the federal state of Tyrol, Austria. METHODS: The simulation-modeling framework consists of an underlying cellular automaton. The cellular automaton model is parameterized by known disease parameters and geographical as well as demographical conditions are included for simulating the spreading. The data generated by simulation are stored in the back room of the data warehouse using the Talend Open Studio software package, and subsequent statistical and data mining tasks are performed using the tool, termed Knowledge Discovery in Database Designer (KD3). RESULTS: The obtained simulation results were used for generating prediction models for all nine federal states of Austria. CONCLUSION: The proposed framework provides a powerful and easy to handle interface for parameterizing and simulating different infectious diseases in order to generate prediction models and improve contingency plans for future events.

Demoting redundant features to improve the discriminatory ability in cancer data.

The identification of a set of relevant but not redundant features is an important first step in building predictive and diagnostic models from biomedical data sets. Most commonly, individual features are ranked in terms of a quality criterion, out of which the best (first) k features are selected. However, feature ranking methods do not sufficiently account for interactions and correlations between the features. Thus, redundancy is likely to be encountered in the selected features. We present a new algorithm, termed Redundancy Demoting (RD), that takes an arbitrary feature ranking as input, and improves this ranking by identifying redundant features and demoting them to positions in the ranking in which they are not redundant. Redundant features are those that are correlated with other features and not relevant in the sense that they do not improve the discriminatory ability of a set of features. Experiments on two cancer data sets, one melanoma image data set and one lung cancer microarray data set, show that our algorithm greatly improves the feature rankings provided by the methods information gain, ReliefF and Student's t-test in terms of predictive power.

A new ensemble-based algorithm for identifying breath gas marker candidates in liver disease using ion molecule reaction mass spectrometry.

MOTIVATION: Alcoholic fatty liver disease (AFLD) and non-AFLD (NAFLD) can progress to severe liver diseases such as steatohepatitis, cirrhosis and cancer. Thus, the detection of early liver disease is essential; however, minimal invasive diagnostic methods in clinical hepatology still lack specificity. RESULTS: Ion molecule reaction mass spectrometry (IMR-MS) was applied to a total of 126 human breath gas samples comprising 91 cases (AFLD, NAFLD and cirrhosis) and 35 healthy controls. A new feature selection modality termed Stacked Feature Ranking (SFR) was developed to identify potential liver disease marker candidates in breath gas samples, relying on the combination of different entropy- and correlation-based feature ranking methods including statistical hypothesis testing using a two-level architecture with a suggestion and a decision layer. We benchmarked SFR against four single feature selection methods, a wrapper and a recently described ensemble method, indicating a significantly higher discriminatory ability of up to 10-15% for the SFR selected gas compounds expressed by the area under the ROC curve (AUC) of 0.85-0.95. Using this approach, we were able to identify unexpected breath gas marker candidates in liver disease of high predictive value. A literature study further supports top-ranked markers to be associated with liver disease. We propose SFR as a powerful tool for biomarker search in breath gas and other biological samples using mass spectrometry. AVAILABILITY: The algorithm SFR and IMR-MS datasets are available under http://biomed.umit.at/page.cfm?pageid=526.

Double photoionization of thiophene and bromine-substituted thiophenes.

We report the double photoionization spectra of thiophene, 3-bromothiophene, and 3,4-dibromothiophene using a coincidence spectroscopy technique based on electron time-of-flight measurements. Spectra have been recorded between the onset and 40.814 eV using He IIalpha radiation. The He I photoelectron spectrum of 3,4-dibromothiophene has also been measured. All the spectra have been analyzed and interpreted in detail on the basis of theoretical simulations from accurate Green's function calculations.

A finite element formulation for atrial tissue monolayer.

OBJECTIVES: Using computer models for the study of complex atrial arrhythmias such as atrial fibrillation is computationally demanding as long observation periods in the order of tens of seconds are required. A well established approach for reducing computational workload is to approximate the thin atrial walls by curved monolayers. On the other hand, the finite element method (FEM) is a well established approach to solve the underlying partial differential equations. METHODS: A generalized 2D finite element method (FEM) is presented which computes the corresponding stiffness and coupling matrix for arbitrarily shaped monolayers (ML). Compared to standard 2D FEM, only one additional coordinate transformation is required. This allows the use of existing FEM software with minor modifications. The algorithm was tested to simulate wave propagation in benchmark geometries and in a model of atrial anatomy. RESULTS: The ML model was able to simulate electric activation in curved tissue with anisotropic conductivity. Simulations in branching tissue yielded slightly different patterns when compared to a volumetric model with finite thickness. In the model of atrial anatomy the computed activation times for five different pacing protocols displayed a correlation of 0.88 compared to clinical data. CONCLUSIONS: The presented method provides a useful and easily implemented approach to model wave propagation in MLs with a few restrictions to volumetric models.

DMSP--Database for Modeling Signaling Pathways. Combining biological and mathematical modeling knowledge for pathways.

OBJECTIVES: Presently, the protein interaction information concerning different signaling pathways is available in a qualitative manner in different online protein interaction databases. The challenge here is to derive a quantitative way of modeling signaling pathways from qualitative way of modeling signaling pathways from a qualitative level. To address this issue we developed a database that includes mathematical modeling knowledge and biological knowledge about different signaling pathways. METHODS: The database is part of an integrative environment that includes environments for pathway design, visualization, simulation and a knowledge base that combines biological and modeling information concerning pathways. The system is designed as a client-server architecture. It contains a pathway designing environment and a simulation environment as upper layers with a relational knowledge base as the underlying layer. RESULTS: DMSP--Database for Modeling Signaling Pathways incorporates biological datasets from online databases like BIND, DIP, PIP, and SPiD. The modeling knowledge that has been incorporated is based on a literature study. Pathway models can be designed, visualized and simulated based on the knowledge stored in the DMSP. The user can download the whole dataset and build pathway models using the knowledge stored in our database. As an example, the TNFalpha pathway model was implemented and tested using this approach. CONCLUSION: DMSP is an initial step towards the aim of combining modeling and biological knowledge concerning signaling pathways. It helps in understanding pathways in a qualitative manner from a qualitative level. Simulation results enable the interpretation of a biological system from a quantitative and system-theoretic point of view.

Patient-specific volume conductor modeling for non-invasive imaging of cardiac electrophysiology.

We propose a general workflow to numerically estimate the spread of electrical excitation in the patients' hearts. To this end, a semi-automatic segmentation pipeline for extracting the volume conductor model of structurally normal hearts is presented. The cardiac electrical source imaging technique aims to provide information about the spread of electrical excitation in order to assist the cardiologist in developing strategies for the treatment of cardiac arrhythmias. The volume conductor models of eight patients were extracted from cine-gated short-axis magnetic resonance imaging (MRI) data. The non-invasive estimation of electrical excitation was compared with the CARTO maps. The development of a volume conductor modeling pipeline for constructing a patient-specific volume conductor model in a fast and accurate way is one essential step to make the technique clinically applicable.

AAM-based segmentation for imaging cardiac electrophysiology.

OBJECTIVES: Activation time (AT) imaging from electrocardiographic (ECG) mapping data has been developing for several years. By coupling 4-dimensional volume data (3D + time) the electrical sequence can be computed non-invasively. In this paper an approach for extracting the ventricular and atrial blood masses for structurally normal hearts by using cine-gated short-axis data obtained via magnetic resonance imaging (MRI) is introduced. METHODS: The blood masses are extracted by employing Active Appearance Models (AAMs). The ventricular blood masses are segmented, applying the AAMs after providing apex cordis and base of the heart in the volume data, whereas the more complex geometry of the atria requires a more specific attempt. On account of this the atrium was divided into three divisions of appearance, where the images of the volume data in the related divisions have a maximum affinity. The first division reaches from the base of the heart to initial visibility of the upper and left lower pulmonary vein. The second division up from there to the last occurrence and the third division from there to the end of the visibility of the right upper and lower pulmonary vein. After extracting the cardiac blood masses the result gets triangulated and remeshed for activation time imaging. RESULTS: With this method the cardiac models of eight patients were extracted and the AT imaging approach was applied to single-beat ECG data of atrial and ventricular depolarization. CONCLUSION: The advantage of the proposed AAM approach is that only a few initial parameters have to be set. Therefore, the approach can be integrated into a processing pipeline that works semi-automatically. The extracted models can be used for further investigations.

[Patient acceptance of magnetic resonance colonography: a prospective inquiry for comparison to conventional colonoscopy]. / Patientenakzeptanz der MR-Kolonographie: Eine prospektive Erhebung im Vergleich zur konventionellen Koloskopie.

BACKGROUND AND OBJECTIVE: Precondition for establishment of magnetic resonance colonography (MRCG) as a diagnostic tool in secondary prevention of colorectal cancer is not only high diagnostic accuracy but also a good acceptance amongst patients. The aim of this study was to compare post-examination appraisal of patients for MRCG to that of bowel preparation and conventional colonoscopy. PATIENTS AND METHODS: 88 patients (24 women, 64 men, mean age 67 +- 17,3 years) were interviewed by a standardized questionnaire regarding pain/discomfort (scale from 1 to 10), overall assessment of difficulties and preference for future tests. After bowel cleansing, MRCG and conventional colonoscopy were performed on the same day. Bowel cleansing consisted of drinking about 5 liters of a polyethylene glycol-electrolyte solution. For MRCG the colon was filled with ca. 2000 ml of tap water. Imaging was performed with a 1.5T MR scanner in the prone position. RESULTS: Most unpleasant for the patients was the preceding bowel preparation (70%), followed by colonoscopy (14%) and MRCG (8%). The preferred method was MRCG (58%) followed by colonoscopy (20,5%). The most unpleasant symptoms named by patients were the amount of oral electrolyte solution that had to be drunk (34%), abdominal pressure (25%), nausea (24%) because of bowel preparation, body positioning (25%) and rectal tube (13%) during MRCG, abdominal pressure (19%) and pain (18%) during colonoscopy. CONCLUSION: Patients' acceptance of MRCG indicates that it has a potential role as an additional diagnostic tool in secondary prevention of colorectal cancer.

Atrial and ventricular myocardium extraction using model-based techniques.

OBJECTIVES: This paper presents an efficient approach for extracting myocardial structures from given atrial and ventricular blood masses to enable non-invasive estimation of electrical excitation in human atria and ventricles. METHODS: Based on given segmented atrial and ventricular blood masses, the approach constructs the myocardial structure directly, in the case that the myocardium can be detected in the volume data, or by using mean model information, in the case that the myocardium cannot be seen in the volume data due to image modalities or artefacts. The approach employs mathematical and gray-value morphology operations. Regulated by the spatial visibility of the myocardial structure in the medical image data especially the atrial myocardium needs to be estimated repeatedly using the a-priori knowledge given by the anatomy. RESULTS: The approach was tested using eight patient data sets. The reconstruction process yielded satisfying results with respect to an efficient generation of a volume conductor model which is essential when trying to implement the estimation of electrical excitation in clinical application. CONCLUSION: The approach yields ventricular and atrial models that qualify for cardiac source imaging in a clinical setting.

A signal processing pipeline for noninvasive imaging of ventricular preexcitation.

OBJECTIVES: Noninvasive imaging of the cardiac activation sequence in humans could guide interventional curative treatment of cardiac arrhythmias by catheter ablation. Highly automated signal processing tools are desirable for clinical acceptance. The developed signal processing pipeline reduces user interactions to a minimum, which eases the operation by the staff in the catheter laboratory and increases the reproducibility of the results. METHODS: A previously described R-peak detector was modified for automatic detection of all possible targets (beats) using the information of all leads in the ECG map. A direct method was applied for signal classification. The algorithm was tuned for distinguishing beats with an adenosine induced AV-nodal block from baseline morphology in Wolff-Parkinson-White (WPW) patients. Furthermore, an automatic identification of the QRS-interval borders was implemented. RESULTS: The software was tested with data from eight patients having overt ventricular preexcitation. The R-peak detector captured all QRS-complexes with no false positive detection. The automatic classification was verified by demonstrating adenosine-induced prolongation of ventricular activation with statistical significance (p <0.001) in all patients. This also demonstrates the performance of the automatic detection of QRS-interval borders. Furthermore, all ectopic or paced beats were automatically separated from sinus rhythm. Computed activation maps are shown for one patient localizing the accessory pathway with an accuracy of 1 cm. CONCLUSIONS: The implemented signal processing pipeline is a powerful tool for selecting target beats for noninvasive activation imaging in WPW patients. It robustly identifies and classifies beats. The small beat to beat variations in the automatic QRS-interval detection indicate accurate identification of the time window of interest.

The challenge of ubiquitous computing in health care: technology, concepts and solutions. Findings from the IMIA Yearbook of Medical Informatics 2005.

OBJECTIVES: To review recent research efforts in the field of ubiquitous computing in health care. To identify current research trends and further challenges for medical informatics. METHODS: Analysis of the contents of the Yearbook on Medical Informatics 2005 of the International Medical Informatics Association (IMIA). RESULTS: The Yearbook of Medical Informatics 2005 includes 34 original papers selected from 22 peer-reviewed scientific journals related to several distinct research areas: health and clinical management, patient records, health information systems, medical signal processing and biomedical imaging, decision support, knowledge representation and management, education and consumer informatics as well as bioinformatics. A special section on ubiquitous health care systems is devoted to recent developments in the application of ubiquitous computing in health care. Besides additional synoptical reviews of each of the sections the Yearbook includes invited reviews concerning E-Health strategies, primary care informatics and wearable healthcare. CONCLUSIONS: Several publications demonstrate the potential of ubiquitous computing to enhance effectiveness of health services delivery and organization. But ubiquitous computing is also a societal challenge, caused by the surrounding but unobtrusive character of this technology. Contributions from nearly all of the established sub-disciplines of medical informatics are demanded to turn the visions of this promising new research field into reality.

Computationally efficient noninvasive cardiac activation time imaging.

OBJECTIVE: The computer model-based computation of the cardiac activation sequence in humans has been recently subject of successful clinical validation. This method is of potential interest for guiding ablation therapy of arrhythmogenic substrates. However, computation times of almost an hour are unattractive in a clinical setting. Thus, the objective is the development of a method which performs the computation in a few minutes run time. METHODS: The computationally most expensive part is the product of the lead field matrix with a matrix containing the source pattern on the cardiac surface. The particular biophysical properties of both matrices are used for speeding up this operation by more than an order of magnitude. A conjugate gradient optimizer was developed using C++ for computing the activation map. RESULTS: The software was tested on synthetic and clinical data. The increase in speed with respect to the previously used Fortran 77 implementation was a factor of 30 at a comparable quality of the results. As an additional finding the coupled regularization strategy, originally introduced for saving computation time, also reduced the sensitivity of the method to the choice of the regularization parameter. CONCLUSIONS: As it was shown for data from a WPWpatient the developed software can deliver diagnostically valuable information at a much shorter span of time than current clinical routine methods. Its main application could be the localization of focal arrhythmogenic substrates.

Enhancing the atom economy of polyketide biosynthetic processes through metabolic engineering.

Polyketides, a large family of bioactive natural products, are synthesized from building blocks derived from alpha-carboxylated Coenzyme A thioesters such as malonyl-CoA and (2S)-methylmalonyl-CoA. The productivity of polyketide fermentation processes in natural and heterologous hosts is frequently limited by the availability of these precursors in vivo. We describe a metabolic engineering strategy to enhance both the yield and volumetric productivity of polyketide biosynthesis. The genes matB and matC from Rhizobium trifolii encode a malonyl-CoA synthetase and a putative dicarboxylate transport protein, respectively. These proteins can directly convert exogenous malonate and methylmalonate into their corresponding CoA thioesters with an ATP requirement of 2 mol per mol of acyl-CoA produced. Heterologous expression of matBC in a recombinant strain of Streptomyces coelicolor that produces the macrolactone 6-deoxyerythronolide B results in a 300% enhancement of macrolactone titers. The unusual efficiency of the bioconversion is illustrated by the fact that approximately one-third of the methylmalonate units added to the fermentation medium are converted into macrolactones. The direct conversion of inexpensive feedstocks such as malonate and methylmalonate into polyketides represents the most carbon- and energy-efficient route to these high value natural products and has implications for cost-effective fermentation of numerous commercial and development-stage small molecules.