Validation and preliminary data from a health-related quality of life questionnaire for owners of dogs with cardiac disease

BACKGROUND: Cardiac disease in dogs impacts the quality of life (QoL) of their owners, but owners\u27 QoL has not been comprehensively assessed in this population. OBJECTIVES: To develop, validate, and provide preliminary data from a health-related QoL (hrQoL) questionnaire for owners of dogs with cardiac disease. SUBJECTS: A total of 141 owners of dogs with cardiac disease were studied. METHODS: An owner hrQoL (O-hrQoL) questionnaire containing 20 items related to areas of a person\u27s life that could be impacted by caring for a dog with cardiac disease was developed and administered to owners of dogs with cardiac disease. The highest possible total score was 100, with higher scores indicating a worse hrQoL. Readability, internal consistency, face and construct validity, and item-total correlations were assessed. RESULTS: Median O-hrQoL score was 35 (range, 0-87). The questionnaire had good internal consistency (Cronbach\u27s alpha = 0.933), construct validity (Spearman\u27s r = 0.38-0.53; Kendall\u27s tau = 0.30-0.43; P \u3c .001), and item-total correlation (Spearman\u27s r = 0.44-0.79; Kendall\u27s tau = 0.34-0.66; all P \u3c .001). Fifty percent of owners indicated a negative effect of dogs\u27 cardiac disease on their own QoL, but all owners responded that caring for their dogs either had strengthened (n = 76; 53.9%) or had no effect on their relationship with their dog (n = 65; 46.1%). CONCLUSIONS AND CLINICAL IMPORTANCE: The O-hrQoL questionnaire had good validity, and results suggest that owners\u27 QoL is significantly impacted by caring for dogs with cardiac disease. Additional research on effective approaches to minimizing the negative effects of a dog\u27s cardiac disease on the owner is warranted

Long-term Incidence and risk of noncardiovascular and all-cause mortality in apparently healthy cats and cats with preclinical hypertrophic cardiomyopathy

Background Epidemiologic knowledge regarding noncardiovascular and all‐cause mortality in apparently healthy cats (AH) and cats with preclinical hypertrophic cardiomyopathy (pHCM) is limited, hindering development of evidence‐based healthcare guidelines. Objectives To characterize/compare incidence rates, risk, and survival associated with noncardiovascular and all‐cause mortality in AH and pHCM cats. Animals A total of 1730 client‐owned cats (722 AH, 1008 pHCM) from 21 countries. Methods Retrospective, multicenter, longitudinal, cohort study. Long‐term health data were extracted by medical record review and owner/referring veterinarian interviews. Results Noncardiovascular death occurred in 534 (30.9%) of 1730 cats observed up to 15.2 years. Proportion of noncardiovascular death did not differ significantly between cats that at study enrollment were AH or had pHCM (P = .48). Cancer, chronic kidney disease, and conditions characterized by chronic weight‐loss‐vomiting‐diarrhea‐anorexia were the most frequently recorded noncardiovascular causes of death. Incidence rates/risk of noncardiac death increased with age in AH and pHCM. All‐cause death proportions were greater in pHCM than AH (65% versus 40%, respectively; P < .001) because of higher cardiovascular mortality in pHCM cats. Comparing AH with pHCM, median survival (study entry to noncardiovascular death) did not differ (AH, 9.8 years; pHCM, 8.6 years; P = .10), but all‐cause survival was significantly shorter in pHCM (P = .0001). Conclusions and Clinical Importance All‐cause mortality was significantly greater in pHCM cats due to disease burden contributed by increased cardiovascular death superimposed upon noncardiovascular death

Trends in extra-corporeal membrane oxygenation for the treatment of acute respiratory distress syndrome in the United States

Medicine, Faculty ofNon UBCAnesthesiology, Pharmacology and Therapeutics, Department ofCritical Care Medicine, Division ofMedicine, Department ofReviewedFacultyGraduateOthe

Applying machine learning to continuously monitored physiological data

Abstract The use of machine learning (ML) in healthcare has enormous potential for improving disease detection, clinical decision support, and workflow efficiencies. In this commentary, we review published and potential applications for the use of ML for monitoring within the hospital environment. We present use cases as well as several questions regarding the application of ML to the analysis of the vast amount of complex data that clinicians must interpret in the realm of continuous physiological monitoring. ML, especially employed in bidirectional conjunction with electronic health record data, has the potential to extract much more useful information out of this currently under-analyzed data source from a population level. As a data driven entity, ML is dependent on copious, high quality input data so that error can be introduced by low quality data sources. At present, while ML is being studied in hybrid formulations along with static expert systems for monitoring applications, it is not yet actively incorporated in the formal artificial learning sense of an algorithm constantly learning and updating its rules without external intervention. Finally, innovations in monitoring, including those supported by ML, will pose regulatory and medico-legal challenges, as well as questions regarding precisely how to incorporate these features into clinical care and medical education. Rigorous evaluation of ML techniques compared to traditional methods or other AI methods will be required to validate the algorithms developed with consideration of database limitations and potential learning errors. Demonstration of value on processes and outcomes will be necessary to support the use of ML as a feature in monitoring system development: Future research is needed to evaluate all AI based programs before clinical implementation in non-research settings

High resolution data modifies intensive care unit dialysis outcome predictions as compared with low resolution administrative data set

High resolution clinical databases from electronic health records are increasingly being used in the field of health data science. Compared to traditional administrative databases and disease registries, these newer highly granular clinical datasets offer several advantages, including availability of detailed clinical information for machine learning and the ability to adjust for potential confounders in statistical models. The purpose of this study is to compare the analysis of the same clinical research question using an administrative database and an electronic health record database. The Nationwide Inpatient Sample (NIS) was used for the low-resolution model, and the eICU Collaborative Research Database (eICU) was used for the high-resolution model. A parallel cohort of patients admitted to the intensive care unit (ICU) with sepsis and requiring mechanical ventilation was extracted from each database. The primary outcome was mortality and the exposure of interest was the use of dialysis. In the low resolution model, after controlling for the covariates that are available, dialysis use was associated with an increased mortality (eICU: OR 2.07, 95% CI 1.75–2.44, p&lt;0.01; NIS: OR 1.40, 95% CI 1.36–1.45, p&lt;0.01). In the high-resolution model, after the addition of the clinical covariates, the harmful effect of dialysis on mortality was no longer significant (OR 1.04, 95% 0.85–1.28, p = 0.64). The results of this experiment show that the addition of high resolution clinical variables to statistical models significantly improves the ability to control for important confounders that are not available in administrative datasets. This suggests that the results from prior studies using low resolution data may be inaccurate and may need to be repeated using detailed clinical data.</jats:p

High resolution data modifies intensive care unit dialysis outcome predictions as compared with low resolution administrative data set

High resolution clinical databases from electronic health records are increasingly being used in the field of health data science. Compared to traditional administrative databases and disease registries, these newer highly granular clinical datasets offer several advantages, including availability of detailed clinical information for machine learning and the ability to adjust for potential confounders in statistical models. The purpose of this study is to compare the analysis of the same clinical research question using an administrative database and an electronic health record database. The Nationwide Inpatient Sample (NIS) was used for the low-resolution model, and the eICU Collaborative Research Database (eICU) was used for the high-resolution model. A parallel cohort of patients admitted to the intensive care unit (ICU) with sepsis and requiring mechanical ventilation was extracted from each database. The primary outcome was mortality and the exposure of interest was the use of dialysis. In the low resolution model, after controlling for the covariates that are available, dialysis use was associated with an increased mortality (eICU: OR 2.07, 95% CI 1.75–2.44, p<0.01; NIS: OR 1.40, 95% CI 1.36–1.45, p<0.01). In the high-resolution model, after the addition of the clinical covariates, the harmful effect of dialysis on mortality was no longer significant (OR 1.04, 95% 0.85–1.28, p = 0.64). The results of this experiment show that the addition of high resolution clinical variables to statistical models significantly improves the ability to control for important confounders that are not available in administrative datasets. This suggests that the results from prior studies using low resolution data may be inaccurate and may need to be repeated using detailed clinical data. Author summary Healthcare administrative databases and disease registries are frequently used in clinical research; however, these sources of data were often not designed for this purpose and lack important detailed clinical data. Therefore, when using these data to answer clinical research questions, important clinical variables are missing and may bias the results. Over the past decade, high resolution databases that integrate administrative information and clinical patient data obtained from electronic health records have been developed specifically for the purpose of clinical research. The purpose of this study is to compare the effects of dialysis on mortality in similar cohorts of critically ill patients with sepsis requiring mechanical ventilation from both an administrative database and from a high resolution database. We found that the addition of clinical variables significantly altered the mortality odds ratio such that it was no longer significant. These results suggest that previous studies using administrative data and repositories may not be valid due to the lack of important clinical variables included in the models

Decontamination of N95 masks for re-use employing 7 widely available sterilization methods.

The response to the COVID-19 epidemic is generating severe shortages of personal protective equipment around the world. In particular, the supply of N95 respirator masks has become severely depleted, with supplies having to be rationed and health care workers having to use masks for prolonged periods in many countries. We sought to test the ability of 7 different decontamination methods: autoclave treatment, ethylene oxide gassing (ETO), low temperature hydrogen peroxide gas plasma (LT-HPGP) treatment, vaporous hydrogen peroxide (VHP) exposure, peracetic acid dry fogging (PAF), ultraviolet C irradiation (UVCI) and moist heat (MH) treatment to decontaminate a variety of different N95 masks following experimental contamination with SARS-CoV-2 or vesicular stomatitis virus as a surrogate. In addition, we sought to determine whether masks would tolerate repeated cycles of decontamination while maintaining structural and functional integrity. All methods except for UVCI were effective in total elimination of viable virus from treated masks. We found that all respirator masks tolerated at least one cycle of all treatment modalities without structural or functional deterioration as assessed by fit testing; filtration efficiency testing results were mostly similar except that a single cycle of LT-HPGP was associated with failures in 3 of 6 masks assessed. VHP, PAF, UVCI, and MH were associated with preserved mask integrity to a minimum of 10 cycles by both fit and filtration testing. A similar result was shown with ethylene oxide gassing to the maximum 3 cycles tested. Pleated, layered non-woven fabric N95 masks retained integrity in fit testing for at least 10 cycles of autoclaving but the molded N95 masks failed after 1 cycle; filtration testing however was intact to 5 cycles for all masks. The successful application of autoclaving for layered, pleated masks may be of particular use to institutions globally due to the virtually universal accessibility of autoclaves in health care settings. Given the ability to modify widely available heating cabinets on hospital wards in well-resourced settings, the application of moist heat may allow local processing of N95 masks