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N. Harris (Ed), UCL Press, London (1992). xxiii + 232 pp. 11.95 [pounds] (pbk). ISBN 1 85728 030 X. The book addresses one of the most important policy areas for [...]

Those Inner Cities: Reconciling the Social and Economic Aims of Urban Policy, B. ROBSON, Oxford University Press, Oxford (1988). xvii + 243 pp. £27.50 (hbk). £8.95 (pbk). ISBN 0 19 874148 0 (hbk). ISBN 0 19 874149 9 (pbk). Urban Land Use and Transport Interaction: Policy and Models, F. V. WEBSTER, P. W. BLY and N. J. PANLLEY (Eds.), Gower, Aldershot, Hants. (1988). xiv + 520 pp. £39.50 (hbk). ISBN 0 566 05726 3. Urban Land Markets in the United Kingdom, Department of the Environment, HMSO, London (1988). 95 pp. £7.00 (pbk). ISBN 0 11 752124 8. The Urbanization of the Third World, J. GUGLER (Ed.), Oxford University Press, Oxford (1988). xi + 421 pp. £12.95 (pbk). ISBN 0 19 823219 4. The Use of Local Plans for Effective Town and Country Planning, M. P. COLLINS and S. MCCONNELL, Bartlett School of Architecture and Planning, University College London (1988). 122 pp. No price given. Management of Innovation in High Technology Small Firms, R. OAKEY, R. ROTHWELL, S. COOPER, Frances Pinter, London (1988). ix + 197 pp. £27.50 (hbk). ISBN 0 86187 672 5. The Unbalanced Recovery, P. ROBINSON, Philip Allen, Oxford (1988). x + 178 pp. £24.50 (hbk). £8.95 (pbk). ISBN 0 86003 0784 (hbk). ISBN 0 86003 181 0 (pbk). Bringing Jobs to People: Employment Promotion at Regional and Local Levels, J. MAYER (Ed.), International Labour Organisation, Geneva (1988). vi + 211 pp. 25 Sw. F. (pbk). ISBN 92 2 106325 9. Input–Output Analysis: Current Developments, M. CIASCHINI (Ed.), Chapman Hall, London (1988). xvi + 413 pp. £45.00 (hbk). ISBN 0 412 28430 8. International Migration: Dynamic Theory and Comparative Analysis, W. WEIDLICH and G. HAAG (Eds.) Springer-Verlag, Berlin (1988). xiv + 387 pp. DM 148 (hbk). ISBN 3 540 18441 4. The Welsh Economy, K. D. GEORGE and L. MAINWARING (Eds.), University of Wales Press, Cardiff (1988). xi + 301 pp. £35.00 (hbk). ISBN 0 7083 1015 X. Western Europe: Economic and Social Studies, the Republic of Ireland, B. Brunt, Paul Chapman, London (1988). xv + 200 pp. £6.95 (pbk). ISBN 1 85396 019 5. Water Resources: Issues and Strategies, A. T. MCDONALD and D. KAY, Longman, Harlow, Essex (1988). 284 pp. £10.95 (pbk). ISBN 0 582 30121 1. Small and Medium Size Enterprises and Region Development, M. GIAOUTZI, P. NIJKAMP and D. STOREY (Eds.), Routledge, Chapman & Hall, Andovr (1988). xvii + 314 pp. £45.00 (hbk). ISBN 0 415004 15. [ABSTRACT FROM PUBLISHER]

Abstract Background Burden of disease studies have been implemented in many countries using the Disability-Adjusted Life Year (DALY) to assess major health problems. Important objectives of the study were to quantify intra-country differentials in health outcomes and to place the United States situation in the international context. Methods We applied methods developed for the Global Burden of Disease (GBD) to data specific to the United States to compute Disability-Adjusted Life Years. Estimates are provided by age and gender for the general population of the United States and for each of the four official race groups: White; Black; American Indian or Alaskan Native; and Asian or Pacific Islander. Several adjustments of GBD methods were made: the inclusion of race; a revised list of causes; and a revised algorithm to allocate cardiovascular disease garbage codes to ischaemic heart disease. We compared the results of this analysis to international estimates published by the World Health Organization for developed and developing regions of the world. Results In the mid-1990s the leading sources of premature death and disability in the United States, as measured by DALYs, were: cardiovascular conditions, breast and lung cancers, depression, osteoarthritis, diabetes mellitus, and alcohol use and abuse. In addition, motor vehicle-related injuries and the HIV epidemic exacted a substantial toll on the health status of the US population, particularly among racial minorities. The major sources of death and disability in these latter populations were more similar to patterns of burden in developing rather than developed countries. Conclusion Estimating DALYs specifically for the United States provides a comprehensive assessment of health problems for this country compared to what is available using mortality data alone.

Continuous clinical grade measurement of SpO 2 in out-of-hospital settings remains a challenge despite the widespread use of photoplethysmography (PPG) based wearable devices for health and wellness applications. This article presents two SpO 2 algorithms: PRR (pulse rate derived ratio-of-ratios) and GPDR (green-assisted peak detection ratio-of-ratios), that utilize unique pulse rate frequency estimations to isolate the pulsatile (AC) component of red and infrared PPG signals and derive SpO 2 measurements. The performance of the proposed SpO 2 algorithms are evaluated using an upper-arm wearable device derived green, red, and infrared PPG signals, recorded in both controlled laboratory settings involving healthy subjects (n=36) and an uncontrolled clinic application involving COVID-19 patients (n=52). GPDR exhibits the lowest root mean square error (RMSE) of 1.6±0.6% for a respiratory exercise test, 3.6 ±1.0% for a standard hypoxia test, and 2.2±1.3% for an uncontrolled clinic use-case. In contrast, PRR provides relatively higher error but with greater coverage overall. Mean error across all combined datasets were 0.2±2.8% and 0.3±2.4% for PRR and GPDR respectively. Both SpO 2 algorithms achieve great performance of low error with high coverage on both uncontrolled clinic and controlled laboratory conditions.

Cytokine release syndrome (CRS) is a noninfec-tious systemic inflammatory response syndrome condition and a principle severe adverse event common in oncology patients treated with immunotherapies. Accurate monitoring and timely prediction of CRS severity remain a challenge. This study presents an XGBoost-based machine learning algorithm for forecasting CRS severity (no CRS, mild- and severe-CRS classes) in the 24 hours following the time of prediction utilizing the common vital signs and Glasgow coma scale (GCS) questionnaire inputs. The CRS algorithm was developed and evaluated on a cohort of patients (n=1,139) surgically treated for neoplasm with no ICD9 codes for infection or sepsis during a collective 9,892 patient-days of monitoring in ICU settings. Different models were trained with unique feature sets to mimic practical monitoring environments where different types of data availability will exist. The CRS models that incorporated all time series features up to the prediction time showcased a micro-average area under curve (AUC) statistic for the receiver operating characteristic curve (ROC) of 0.94 for the 3 classes of CRS grades. Models developed on a second cohort requiring data within the 24 hours preceding prediction time showcased a relatively lower 0.88 micro-average AUROC as these models did not benefit from implicit information in the data availability. Systematic removal of blood pressure and/or GCS inputs revealed significant decreases (p<0.05) in model performances that confirm the importance of such features for CRS prediction. Accurate CRS prediction and timely intervention can reverse CRS adverse events and maximize the benefit of immunotherapies in oncology patients.

This work presents automated apnea event de-tection using blood oxygen saturation (SpO2) and pulse rate (PR), conveniently recorded with a pulse oximeter. A large, diverse cohort of patients (n=8068, age≥40 years) from the sleep heart health study dataset with annotated sleep events have been employed in this study. A deep-learning model is trained to detect apnea in successive 30 s epochs and performances are assessed on two independent sub-cohorts of test data. The proposed algorithm showcases the highest test performance of 90.4 % area under the receiver operating characteristic curve and 58.9% area under the precision-recall curve for epoch-based apnea detection. Additionally, the model consistently performs well across various apnea subtypes, with the highest sensitivity of 93.4 % for obstructive apnea detection followed by 90.5 % for central apnea and 89.1 % for desaturation associated hypopnea. Overall, the proposed algorithm provides a robust and sensitive approach for sleep apnea event detection using a noninvasive pulse oximeter sensor. Clinical Relevance - The study establishes high sensitivity for automated epoch-based apnea detection across a diverse study cohort with various comorbidities using simply a pulse oximeter. This highly cost-effective approach could also enable convenient sleep and health monitoring over long-term.

In this article, we describe how innovation contests-a vehicle to crowdsource ideas and problem-solving efforts-propelled frontline employees to exert discretionary efforts in organizational problem-solving at Massachusetts General Hospital. As designers and administrators of four innovation contests in three disease centers, we share firsthand knowledge of how the contests enabled clinicians and administrative staff, whose primary job is delivering high-quality patient care, to become involved in ideation, selection, and implementation of their own ideas. We describe the processes that we designed and implemented, ideas that these processes generated, and findings from interviewing employees about their experiences afterwards. Our findings suggest that the benefits of implementing innovation contests were multifaceted. To employees, the contests provided a platform to voice suggestions and participate in any aspect of the innovation process that they found interesting. To managers, the contests revealed real, empirical issues affecting operation and patient care based on frontline employees' knowledge. To the organization as a whole, the contests promoted collaborative problem-solving among likeminded, innovative employees., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Background: Acute myocardial infarction (AMI) is a common cause of hospital admissions, readmissions, and mortality worldwide. Digital health interventions (DHIs) that promote self-management, adherence to guideline-directed therapy, and cardiovascular risk reduction may improve health outcomes in this population. The "Corrie" DHI consists of a smartphone application, smartwatch, and wireless blood pressure monitor to support medication tracking, education, vital signs monitoring, and care coordination. We aimed to assess the cost-effectiveness of this DHI plus standard of care in reducing 30-day readmissions among AMI patients in comparison to standard of care alone., Methods: A Markov model was used to explore cost-effectiveness from the hospital perspective. The time horizon of the analysis was 1 year, with 30-day cycles, using inflation-adjusted cost data with no discount rate. Currencies were quantified in US dollars, and effectiveness was measured in quality-adjusted life-years (QALYs). The results were interpreted as an incremental cost-effectiveness ratio at a threshold of $100,000 per QALY. Univariate sensitivity and multivariate probabilistic sensitivity analyses tested model uncertainty., Results: The DHI reduced costs and increased QALYs on average, dominating standard of care in 99.7% of simulations in the probabilistic analysis. Based on the assumption that the DHI costs $2750 per patient, use of the DHI leads to a cost-savings of $7274 per patient compared with standard of care alone., Conclusions: Our results demonstrate that this DHI is cost-saving through the reduction of risk for all-cause readmission following AMI. DHIs that promote improved adherence with guideline-based health care can reduce hospital readmissions and associated costs., Competing Interests: Under a license agreement between Corrie Health and the Johns Hopkins University, the University owns equity in Corrie Health, and the University and Drs F.A.M., M.A.L., and S.S.M. are entitled to royalty distributions related to the technology described in the study discussed in this publication. In addition, Drs F.A.M., M.A.L., and S.S.M. are co-founders of and hold equity in Corrie Health. This arrangement has been reviewed and approved by Johns Hopkins University in accordance with its conflict of interest policies. S.S.M. reports consulting in the last 36 months with Amgen, AstraZeneca, Esperion, iHealth, Kaneka, Novo Nordisk, Sanofi, Regeneron, REGENXBIO, and 89bio. He is a co-inventor on a system to estimate LDL cholesterol levels, patent application pending. W.V.P. reports consulting with Monument Analytics. The remaining authors declare no conflict of interest., (Copyright © 2021 The Author(s). Published by Wolters Kluwer Health, Inc.)

Background: Thirty-day readmissions among patients with acute myocardial infarction (AMI) contribute to the US health care burden of preventable complications and costs. Digital health interventions (DHIs) may improve patient health care self-management and outcomes. We aimed to determine if patients with AMI using a DHI have lower 30-day unplanned all-cause readmissions than a historical control., Methods: This nonrandomized controlled trial with a historical control, conducted at 4 US hospitals from 2015 to 2019, included 1064 patients with AMI (DHI n=200, control n=864). The DHI integrated a smartphone application, smartwatch, and blood pressure monitor to support guideline-directed care during hospitalization and through 30-days post-discharge via (1) medication reminders, (2) vital sign and activity tracking, (3) education, and (4) outpatient care coordination. The Patient Activation Measure assessed patient knowledge, skills, and confidence for health care self-management. All-cause 30-day readmissions were measured through administrative databases. Propensity score-adjusted Cox proportional hazard models estimated hazard ratios of readmission for the DHI group relative to the control group., Results: Following propensity score adjustment, baseline characteristics were well-balanced between the DHI versus control patients (standardized differences <0.07), including a mean age of 59.3 versus 60.1 years, 30% versus 29% Women, 70% versus 70% White, 54% versus 54% with private insurance, 61% versus 60% patients with a non ST-elevation myocardial infarction, and 15% versus 15% with high comorbidity burden. DHI patients were predominantly in the highest levels of patient activation for health care self-management (mean score 71.7±16.6 at 30 days). The DHI group had fewer all-cause 30-day readmissions than the control group (6.5% versus 16.8%, respectively). Adjusting for hospital site and a propensity score inclusive of age, sex, race, AMI type, comorbidities, and 6 additional confounding factors, the DHI group had a 52% lower risk for all-cause 30-day readmissions (hazard ratio, 0.48 [95% CI, 0.26-0.88]). Similar results were obtained in a sensitivity analysis employing propensity matching., Conclusions: Our results suggest that in patients with AMI, the DHI may be associated with high patient activation for health care self-management and lower risk of all-cause unplanned 30-day readmissions. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT03760796.

The field of cardiac electrophysiology has been on the cutting edge of advanced digital technologies for many years. More recently, medical device development through traditional clinical trials has been supplemented by direct to consumer products with advancement of wearables and health care apps. The rapid growth of innovation along with the mega-data generated has created challenges and opportunities. This review summarizes the regulatory landscape, applications to clinical practice, opportunities for virtual clinical trials, the use of artificial intelligence to streamline and interpret data, and integration into the electronic medical records and medical practice. Preparation of the new generation of physicians, guidance and promotion by professional societies, and advancement of research in the interpretation and application of big data and the impact of digital technologies on health outcomes will help to advance the adoption and the future of digital health care.

Determining whether a patient has taken a direct oral anticoagulant (DOAC) is critical during the periprocedural and preoperative period in the emergency department. However, the inaccessibility of complete medical records, along with the generally inconsistent sensitivity of conventional coagulation tests to these drugs, complicates clinical decision making and puts patients at risk of uncontrollable bleeding. In this study, we evaluate the utility of inhibitor-II-X (i-II-X), a novel, microfluidics-based diagnostic assay for the detection and identification of Factor Xa inhibitors (FXa-Is) in an acute care setting., Design: First-in-human, 91-patient, single-center retrospective pilot study., Setting: Emergency room., Patients: Adult patients admitted into the emergency department, which received any clinician-ordered coagulation test requiring a 3.2% buffered sodium citrate blood collection tube., Interventions: None., Measurements and Main Results: Plasma samples from patients admitted to the emergency department were screened for the use of FXa-Is, including apixaban and rivaroxaban, within the past 24 hours using our new i-II-X microfluidic test. i-II-X results were then compared with results from conventional coagulation tests, including prothrombin time (PT) and international normalized ratio (INR), which were ordered by treating clinicians, and an anti-Xa assay for rivaroxaban. The i-II-X test detected DOACs in samples collected from the emergency department with 95.20% sensitivity and 100.00% specificity. Unlike PT and INR, i-II-X reliably identified patients who had prolonged clotting times secondary to the presence of a FXa-I., Conclusions: The i-II-X test overcomes the limitations of currently available coagulation tests and could be a useful tool by which to routinely screen patients for DOACs in emergency and critical care settings. Our new diagnostic approach is particularly relevant in clinical situations where medical records may be unavailable, or where precautions need to be taken prior to invasive interventions, such as specific reversal agent administration., Competing Interests: Drs. Frydman, Tompkins, and Toner are named as inventors on patent applications, which are directed, in part, to technology discussed in this article and from which Drs. Frydman, Tompkins, and Toner may benefit in the future. The remaining authors have disclosed that they do not have any conflicts of interest., (Copyright © 2019 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine.)

Automatic classification of abnormal beats in ECG signals is crucial for monitoring cardiac conditions and the performance of the classification will improve the success rate of the treatment. However, under certain circumstances, traditional classifiers cannot be adapted well to the variation of ECG morphologies or variation of different patients due to fixed hand-crafted features selection. Additionally, existing deep learning related solutions reach their limitation because they fail to use the beat-to-beat information together with single-beat morphologies. This paper applies a novel solution which converts one-dimensional ECG signal into spectro-temporal images and use multiple dense convolutional neural network to capture both beat-to-beat and single-beat information for analysis. The results of simulation on the MIT-BIH arrhythmias database demonstrate the effectiveness of the proposed methodology by showing an outstanding detection performance compared to other existing methods.

More than 50% of the whole world lives with chronic diseases leading to a global economic burden of 47 trillion dollars. Healthcare organizations are moving towards managing patients outside hospital, thereby improving patient safety and quality of life. Current at-home ambulatory remote monitoring analytics based on population level thresholds of individuals physiology have shown poor outcomes and high degree of false alarm burden. The personalized multivariate physiology analytics leverages readily-available low-cost wearable biosensors to detect subtle physiology changes precursor of patient's health deterioration. In this paper we present a novel personalized multivariate physiology analytics for remote patient monitoring in an ambulatory setting. We also present our verification and validation results using perturbation testing along with clinical trial results.

Background: Unplanned readmissions after hospitalization for acute myocardial infarction are among the leading causes of preventable morbidity, mortality, and healthcare costs. Digital health interventions could be an effective tool in promoting self-management, adherence to guideline-directed therapy, and cardiovascular risk reduction. A digital health intervention developed at Johns Hopkins-the Corrie Health Digital Platform (Corrie)-includes the first cardiology Apple CareKit smartphone application, which is paired with an Apple Watch and iHealth Bluetooth-enabled blood pressure cuff. Corrie targets: (1) self-management of cardiac medications, (2) self-tracking of vital signs, (3) education about cardiovascular disease through articles and animated videos, and (4) care coordination that includes outpatient follow-up appointments., Methods and Results: The 3 phases of the MiCORE study (Myocardial infarction, Combined-device, Recovery Enhancement) include (1) the development of Corrie, (2) a pilot study to assess the usability and feasibility of Corrie, and (3) a prospective research study to primarily compare time to first readmission within 30 days postdischarge among patients with Corrie to patients in the historical standard of care comparison group. In Phase 2, the feasibility of deploying Corrie in an acute care setting was established among a sample of 60 patients with acute myocardial infarction. Phase 3 is ongoing and patients from 4 hospitals are being enrolled as early as possible during their hospital stay if they are 18 years or older, admitted with acute myocardial infarction (ST-segment-elevation myocardial infarction or type I non-ST-segment-elevation myocardial infarction), and own a smartphone. Patients are either being enrolled with their own personal devices or they are provided an iPhone and/or Apple Watch for the duration of the study. Phase 3 started in October 2017 and we aim to recruit 140 participants., Conclusions: This article will provide an in-depth understanding of the feasibility associated with implementing a digital health intervention in an acute care setting and the potential of Corrie as a self-management tool for acute myocardial infarction recovery.

Aim: To determine if patients in cardiology practices would be interested in or willing to use mobile health technologies., Methods: Patients seen at an ambulatory cardiology clinic for any indication were included. A paper survey was administered during pre-intake that assessed frequency of use, familiarity with and interest in mobile health applications. Data were analyzed using an exploratory logistic regression analysis to determine demographic predictors for technology utilization., Results: A total of 306 patients were included (a plurality, 39.3%, in age group 50-69; 62.7% male). Those from median household incomes between US$30,000 and US$74,999 and those 18-29 years old were more likely to have used a health app (0.53 and 1.21, respectively). Those between 18 and 29 years were less interested in virtual visits with their healthcare provider (-0.92) and those over age 70 were less comfortable using their phone apps (-0.80)., Conclusion: Age and income are important predictors of mobile health app adoption.

Between 7-18 million Americans suffer from sleep disordered breathing (SDB), including those who suffer from obstructive sleep apnea (OSA). Despite this high prevalence and burden of OSA, existing diagnostic techniques remain impractical for widespread screening. In this study, we introduce a new model for OSA screening and describe an at-home wearable sleep mask (named ARAM) that can robustly track the wearers' sleep patterns. This monitoring is achieved using select sensors that enable screening and monitoring in a form-factor that can be easily self-instrumented. Based on feedback from sleep doctors and technicians, we incorporate the most valuable sensors for OSA diagnosis, while maintaining ease-of-use and comfort for the patient. We discuss the results of preliminary field trials, where both our sleep mask and a commercially available device were worn simultaneously to evaluate our device's robustness. Based on these results, we discuss next steps for the design of the screening system, including analyses techniques that would provide more efficient screening than existing systems.

Background: Macrophages predominate among the inflammatory cells in rejecting allografts. These innate immune cells, in addition to allospecific T cells, can damage cardiomyocytes directly., Methods and Results: We explored whether sensitive positron emission tomography-computed tomography (PET-CT) imaging of macrophages-avid nanoparticles detects rejection of heart allografts in mice. In addition, we used the imaging method to follow the immunomodulatory impact of angiotensin-converting enzyme inhibitor therapy on myeloid cells in allografts. Dextran nanoparticles were derivatized with the PET isotope copper-64 and imaged 7 days after transplantation. C57BL/6 recipients of BALB/c allografts displayed robust positron emission tomography signal (standard uptake value allograft, 2.8±0.3; isograft control, 1.7±0.2; P<0.05). Autoradiography and scintillation counting confirmed the in vivo findings. We then imaged the effects of angiotensin-converting enzyme inhibitor (5 mg/kg enalapril). Angiotensin-converting enzyme inhibitor significantly decreased nanoparticle signal (P<0.05). Histology and flow cytometry showed a reduced number of myeloid cells in the graft, blood, and lymph nodes and diminished antigen presentation (P<0.05 versus untreated allografts). Angiotensin-converting enzyme inhibitor also significantly prolonged allograft survival (12 versus 7 days; P<0.0001)., Conclusions: Nanoparticle macrophage PET-CT detects heart transplant rejection and predicts organ survival by reporting on myeloid cells.

Atrial fibrillation (AF) is a common cardiac arrhythmia with significant morbidity and public health cost. Because of limitations of efficacy and safety of conventional antiarrhythmic agents, alternative therapies for AF are needed. The potential antiarrhythmic properties of lipid-altering therapy, including the 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitors and fish oils, are increasingly recognized, particularly in light of their potential anti-inflammatory properties. This review examines the known effects of lipid-altering therapy on atrial arrhythmias in both experimental and clinical settings. Inflammatory states, such as post-cardiac surgery and AF of recent onset, show promise as targets. In contrast, lipid-lowering therapy is less likely to affect longstanding persistent AF. Current recommendations for the use of lipid-altering therapy for prevention and treatment of AF are summarized.

Background: Burden of disease studies have been implemented in many countries using the Disability-Adjusted Life Year (DALY) to assess major health problems. Important objectives of the study were to quantify intra-country differentials in health outcomes and to place the United States situation in the international context., Methods: We applied methods developed for the Global Burden of Disease (GBD) to data specific to the United States to compute Disability-Adjusted Life Years. Estimates are provided by age and gender for the general population of the United States and for each of the four official race groups: White; Black; American Indian or Alaskan Native; and Asian or Pacific Islander. Several adjustments of GBD methods were made: the inclusion of race; a revised list of causes; and a revised algorithm to allocate cardiovascular disease garbage codes to ischaemic heart disease. We compared the results of this analysis to international estimates published by the World Health Organization for developed and developing regions of the world., Results: In the mid-1990s the leading sources of premature death and disability in the United States, as measured by DALYs, were: cardiovascular conditions, breast and lung cancers, depression, osteoarthritis, diabetes mellitus, and alcohol use and abuse. In addition, motor vehicle-related injuries and the HIV epidemic exacted a substantial toll on the health status of the US population, particularly among racial minorities. The major sources of death and disability in these latter populations were more similar to patterns of burden in developing rather than developed countries., Conclusion: Estimating DALYs specifically for the United States provides a comprehensive assessment of health problems for this country compared to what is available using mortality data alone.