Daily Hospital Research Articles

A Retrospective Health Economic Analysis of a Stable Hypochlorous Acid Preserved Wound Cleanser Versus 0.9% Saline Solution as Instillation for Negative-Pressure Wound Therapy in Severe and Infected Wounds.

IntroductionNegative-pressure wound therapy (NPWT) with instillation and dwell time is an accepted adjunct therapy for infected wounds. A study was conducted to assess whether the use of hypochlorous acid preserved wound cleanser (HAPWOC) (Vashe, Urgo Medical North America, Fort Worth, TX, USA) as the irrigant would reduce the cost of care in comparison to 0.9% saline (NaCl).MethodA comparative, observational, retrospective analysis assessed 27 serious and infected wounds in 24 patients. The lesions were of different and complex etiologies, including necrotizing fasciitis and stage IV diabetic foot ulcers. NPWT was used as part of the overall multimodal treatment regimen. The only variance in the treatment protocol was the use of saline (N=8) or HAPWOC (N=19) as the irrigant.ResultsWhen compared to NaCl, wounds treated with HAPWOC trended toward fewer operating room (OR) visits versus NaCl (3.3 versus 4.1) and a shorter length of hospital stay (LOS) (24.3 days versus 37.9 days).The Orlando Health Transparency guide shows the cost of OR debridement as $2,525. Thus, debridement for HAPWOC-treated wounds ($8,332) costs $2,020 (24%) less than for NaCl-treated wounds ($10,352).Using the 2016 Kaiser Health data (average daily hospital cost, excludingall interventions: $2,052), the cost of HAPWOC and NaCl instill translates to $49,864 and $77,771, respectively, a difference of $27,906 (56%) more for NaCl treatment. The Agency for Healthcare Research and Quality (AHRQ) 2012 data indicate an average daily cost of hospital stay, including all interventions, of $10,400. Thus, HAPWOC treatment cost translates to $252,720 versus NaCl-related costs of $394,160; in these calculations, using NaCl costs $141.440 (+56%) more per patient than HAPWOC.ConclusionThe use of NPWT with HAPWOC versus NaCl as instillation in NPWT reduces the number of visits to the operating room and LOS. This has a significant impact on lowering the cost of care when HAPWOC is used.

Short-term effects of air pollutants on hospital admissions for acute bronchitis in children: a multi-city time-series study in Southwest China.

Few studies have investigated the effects of air pollutants on children with acute bronchitis. This study aimed to explore the acute effects of four air pollutants [fine particulate matter (PM2.5), inhalable particulate matter (PM10), sulfur dioxide (SO2), and nitrogen dioxide (NO2)] on the daily number of children admitted to the hospital for acute bronchitis in Sichuan Province, China. The 49,975 records of hospitalized children with acute bronchitis from medical institutions in nine cities/prefectures, Sichuan Province, China, as well as the simultaneous meteorological data and air pollution data from 183 monitoring sites, were collected from 1 January 2017 to 31 December 2018. A generalized additive model was adopted to analyze the exposure-response and lag effects of hospitalizations of children with acute bronchitis to air pollutants. Stratified analyses were conducted based on sex, age, and season. The single-pollutant model showed that a 10µg/m3 increase at lag07 of PM2.5, PM10, SO2, and NO2 corresponded to an increase of 1.23% [95% confidence interval (CI) 0.21-2.26%], 1.33% (95% CI 0.62-2.05%), 23.52% (95% CI 11.52-36.81%), and 12.47% (95% CI 8.46-16.64%) in daily hospitalizations for children with acute bronchitis, respectively. Children aged 0-2 were more prone to PM2.5 (P < 0.05). Interestingly, the effects were stronger in the warm season than in transition seasons and the cool season for PM2.5 and PM10 (P < 0.05). The higher daily average concentrations of four pollutants in Sichuan Province can result in an increased number of children hospitalized for acute bronchitis.

Air pollution and hospitalization of patients with idiopathic pulmonary fibrosis in Beijing: a time-series study

BackgroundA small number of studies suggested that air pollution was associated with idiopathic pulmonary fibrosis (IPF) exacerbation, incidence and mortality. However, no studies to date were conducted in regions where air pollution is substantial. We aimed to investigate whether there are associations between acute increases in air pollution and hospitalization of patients with a confirmed primary diagnosis of IPF in Beijing.MethodsDaily count of IPF hospitalizations (International Classification of Disease-10th Revision, J84.1) was obtained from an administrative database for 2013–2017 while daily city-wide average concentrations of PM10, PM2.5, NO2, Ozone, SO2 were obtained from 35 municipal monitoring stations for the same period. The association between daily IPF hospitalization and average concentration of each pollutant was analyzed with a generalized additive model estimating Poisson distribution.ResultsDaily 24-h mean PM2.5 concentration during 2013–2017 was 76.7 μg/m3. The relative risk (RR) of IPF hospitalization per interquartile range (IQR) higher (72 μg/m3) in PM2.5 was 1.049 (95% CI 1.024–1.074) and 1.031 (95% CI 1.007–1.056) for lag0 and moving averages 0–1 days respectively. No significant associations were observed for other lags. Statistically significant positive associations were also observed at lag0 with SO2, Ozone and NO2 (in men only). Positive associations were seen at moving averages 0–30 days for PM10 (RR per 86 μg/m3: 1.021, 95% CI 0.994–1.049), NO2 (RR per 30 μg/m3: 1.029, 95% CI 0.999–1.060), and SO2 (RR per 15 μg/m3: 1.060 (95% CI 1.025–1.097), but not with PM2.5 or Ozone.ConclusionsDespite improvement in air quality since the implementation of clean air policy in 2013, acute exposure to higher levels of air pollution is significantly associated with IPF hospitalization in Beijing. Air quality policy should be continuously enforced to protect vulnerable IPF populations as well as the general public.

Air Temperature Affects the Hospital Admission for Cardiovascular Diseases among Rural Residents in Dingxi City

Objective To explore the effect of air temperature on the hospitalization of rural residents with cardiovascular diseases and its lag effect in Dingxi city. Methods The meteorological data and air pollution data of Dingxi city from 2018 to 2019,as well as the daily hospitalization data of rural residents due to cardiovascular diseases,were collected.The distributed lag non-linear models were employed to analyze the relationship between daily mean air temperature and the number of inpatients with cardiovascular diseases.Meanwhile,stratified analysis was carried out according to gender,age,and disease. Results There was a non-linear relationship between air temperature and the number of hospitalized rural residents with cardiovascular diseases in Dingxi city.The exposure-response curve approximated a bell shape.The curves for different cardiovascular diseases appeared similar shapes,with different temperature thresholds.Low temperature(-7 ℃) and moderately low temperature(0 ℃) exhibited a cumulative lag effect on the number of patients hospitalized with cardiovascular diseases.With a cumulative lag of 7 days at -7 ℃ and 14 days at 0 ℃,the RR values peaked,which were 1.121(95% CI=1.002-1.255) and 1.198(95% CI=1.123-1.278),respectively.With a cumulative lag of 14 days at 0 ℃,the RR values were 1.034(95% CI=1.003-1.077) and 1.039(95% CI=1.004-1.066) for the number of hospitalized patients with ischemic heart disease and heart rhythm disorders,respectively.The cumulative lag effects of moderately high temperature(17 ℃) and high temperature(21 ℃) on ischemic heart disease,heart rhythm disorders,and cerebrovascular disease all peaked on that day.Specifically,the RR values at 17 ℃ and 21 ℃ were 1.148(95% CI=1.092-1.206) and 1.176(95% CI=1.096-1.261) for ischemic heart disease,1.071(95% CI=1.001-1.147) and 1.112(95% CI=1.011-1.223) for heart rhythm disorders,and 1.084(95% CI=1.025-1.145) and 1.094(95% CI=1.013-1.182) for cerebrovascular disease,respectively.There was no cumulative lag effect of air temperature on the number of hospitalized patients with heart failure.In addition,stratified analysis showed that low temperature(-7 ℃) and moderately low temperature(0 ℃) affected the number of hospitalized female patients with cardiovascular diseases,and only moderately low temperature(0 ℃) affected males.The cumulative lag effect of high temperature on females was higher than that on males.Air temperature exhibited a stronger impact on female patients than on male patients. Additionally,the population aged<65 years old was more sensitive to low temperature and high temperature than that aged ≥65 years old. Conclusions Air temperature changes increase the hospitalization risk of rural residents with cardiovascular diseases in Dingxi city,which presents a lag effect.The effects of air temperature on patients hospitalized due to cardiovascular diseases varied among different etiologies,genders,and ages.It is necessary to emphasize on the impact of temperature changes on health in residents,especially for key populations such as females,people aged<65 years old,and those with ischemic heart disease.

Association Between Sulfur Dioxide and Daily Inpatient Visits With Respiratory Diseases in Ganzhou, China: A Time Series Study Based on Hospital Data.

BackgroundSulfur dioxide (SO2) has been reported to be related to the mortality of respiratory diseases, but the relationship between SO2 and hospital inpatient visits with respiratory diseases and the potential impact of different seasons on this relationship is still unclear.MethodsThe daily average concentrations of air pollutants, including SO2 and meteorological data in Ganzhou, China, from 2017 to 2019 were collected. The data on daily hospitalization for respiratory diseases from the biggest hospital in the city were extracted. The generalized additive models (GAM) and the distributed lag non-linear model (DLNM) were employed to evaluate the association between ambient SO2 and daily inpatient visits for respiratory diseases. Stratified analyses by gender, age, and season were performed to find their potential effects on this association.ResultsThere is a positive exposure-response relationship between SO2 concentration and relative risk of respiratory inpatient visits. Every 10 μg/m3 increase in SO2 was related to a 3.2% (95% CI: 0.6–6.7%) exaltation in daily respiratory inpatient visits at lag3. In addition, SO2 had a stronger association with respiratory inpatient visits in women, older adults (≥65 years), and warmer season (May-Oct) subgroups. The relationship between SO2 and inpatient visits for respiratory diseases was robust after adjusting for other air pollutants, including PM10, NO2, O3, and CO.ConclusionThis time-series study showed that there is a positive association between short-term SO2 exposure and daily respiratory inpatient visits. These results are important for local administrators to formulate environmental public health policies.

Association between ambient air pollution and daily hospital visits for cardiovascular diseases in Wuhan, China: a time-series analysis based on medical insurance data

ABSTRACT Although evidence showed the adverse effects of air pollution on cardiovascular disease (CVDs), few studies were based on medically insured populations. We applied a generalized additive Poisson model (GAM) to estimate the short-term effects of ambient air pollution on a group of medically insured population in Wuhan, China. We extracted daily air pollution data, meteorological data, and daily hospital visits for CVDs. We found that the ambient air pollutants sulfur dioxide (SO2), nitrogen dioxide (NO2), ground-level ozone (O3) particulate matter (PM) with an aerodynamic diameter ≤10 μm (PM10), and those ≤2.5 μm (PM2.5) all increased the risk of daily hospital visits for CVDs. We also found that the effect of air pollution on daily hospital visits for CVDs is greater in the cold season than in the warm season. Our findings can be used as evidence that supports the formulation of policies for air pollution and CVDs.

A ensemble methodology for automatic classification of chest X-rays using deep learning

Chest radiographies, or chest X-rays, are the most standard imaging exams used in daily hospitals. Responsible for assisting in detecting numerous pathologies and findings that directly interfere in the patient's life, this exam is therefore crucial in screening patients. This work proposes a methodology based on a Convolutional Neural Networks (CNNs) ensemble to aid the diagnosis of chest X-ray exams by screening them with a high probability of being normal or abnormal. In the development of this study, a private dataset with frontal and lateral projections X-ray images was used. To build the ensemble model, VGG-16, ResNet50 and DenseNet121 architectures, which are commonly used in the classification of Chest X-rays, were evaluated. A Confidence Threshold (CTR) was used to define the predictions into High Confidence Normal (HCn), Borderline classification (BC), or High Confidence Abnormal (HCa). In the tests performed, very promising results were achieved: 54.63% of the exams were classified with high confidence; of the normal exams, 32% were classified as HCn with an false discovery rate (FDR) of 1.68%; and as to the abnormal exams, 23% were classified as HCa with 4.91% false omission rate (FOR).

Implementing the Robotic deep inferior epigastric perforator Flap in daily practice: A series of 10 cases.

The deep inferior epigastric perforator (DIEP) flap is the workhorse in microvascular breast reconstruction. Rectus muscle sacrifice or denervation and inappropriate rectus sheath closure are the main causes of abdominal wall morbidity. Robotic vessel dissection may limit fascial incision length below the arcuate line and rectus denervation. This study describes and focuses on the implementation and feasibility of this technique in daily surgical practice. A retrospective data collection of all robotic DIEP flap reconstructions between August 2020 and October 2021 was conducted at our university hospital. Primary outcome variables were flap success and intraoperative complications. Secondary variables included timing of the different robot-assisted stages, total operating time, and postoperative complications within 6 weeks. In total, 10 robotic DIEPs were performed using a multiport robotic system through the transabdominal preperitoneal approach. Nine short intramuscular (SIM) and 1 subfascial (SF) perforators were selected based on the preoperative imaging. There were no perioperative intra-abdominal complications or flap losses. Robotic installation ranges from 16 to 40min. Mean robot-assisted operating time was 86min, and mean operative time was 479min. We present the largest case series of robotic DIEP flap reconstructions for delayed and immediate autologous breast reconstruction through a transabdominal preperitoneal approach. This technique is safe, reproducible, and feasible in a daily hospital setting. We have observed a steep learning curve associated with robotic vessel dissection. Future research may optimize surgical technique, assess possible benefit on abdominal wall morbidity, and evaluate cost-effectiveness.

Effect of apparent temperature on hospitalization from a spectrum of cardiovascular diseases in rural residents in Fujian, China

Cardiovascular disease (CVD) is a leading threat to global public health. Although associations between temperature and CVD hospitalization have been suggested for developed countries, limited evidence is available for developing countries or rural residents. Moreover, the effect of apparent temperature (AT) on the spectrum of cause-specific CVDs remains unknown. Based on 2,024,147 CVD hospitalizations for rural residents from eight regions in Fujian Province, China, during 2010–2016, a quasi-Poisson regression with distributed lag non-linear model was fitted to estimate the AT effect on daily CVD hospitalization for each region, and then pooled in a meta-regression that included regional indicators related to rural residents. Stratified analyses were performed according to the cause of hospitalization, sex and age groups. Finally, we calculated the fraction of CVD hospitalizations attributable to AT, as a reflection of the burden associated with AT. The heat effect appeared at lag 0–1 days, with 19% (95% CI, 11–26%) increased risk of CVD hospitalization, which was worse for ischemic heart disease, heart failure, arrhythmias and ischemic stroke. The decreased AT was associated with increase of hemorrhagic stroke at lag 0–28 days. People aged 65 and above suffered more from the heat effect on cardiovascular and cerebrovascular diseases. Regions with a lower gross value of agricultural production, rural residents’ per capita net income, number of air conditioners and water heaters were more susceptible. A large number of hospitalizations were attributable to heat for most subcategories. High AT level increased CVD hospitalization, and the subcategories had different susceptibilities. The effects were modified by individual and regional characteristics. These findings have important implications for the development of targeted interventions and for hospital service planning.

A cukorbeteg láb szövődményeinek ellátása a nemzetközi irányelv alapján

The prevalence of diabetes continues to increase worldwide, leading to a rising incidence of foot complications (neuropathy, angiopathy ulcus, infections). In the prevention of the diabetic foot complications the correct diagnosis of the actual conditions is essential, because the infected foot ulcers can lead to amputations of the lower extremities. Diabetic foot ulcers and infections are treated with daily wound care, antimicrobial therapy, surgical treatment, off-loading processes, hospitalization and rehabilitation. These processes are associated with high health care costs. In this paper the basic principles of prevention, classification and treatment of diabetic foot ulcers are summarized based on the publication of the International Working Group on the Diabetic Foot (IWGDF).

Nowcasting (Short-Term Forecasting) of COVID-19 Hospitalizations Using Syndromic Healthcare Data, Sweden, 2020.

We report on local nowcasting (short-term forecasting) of coronavirus disease (COVID-19) hospitalizations based on syndromic (symptom) data recorded in regular healthcare routines in Östergötland County (population ≈465,000), Sweden, early in the pandemic, when broad laboratory testing was unavailable. Daily nowcasts were supplied to the local healthcare management based on analyses of the time lag between telenursing calls with the chief complaints (cough by adult or fever by adult) and COVID-19 hospitalization. The complaint cough by adult showed satisfactory performance (Pearson correlation coefficient r>0.80; mean absolute percentage error <20%) in nowcasting the incidence of daily COVID-19 hospitalizations 14 days in advance until the incidence decreased to <1.5/100,000 population, whereas the corresponding performance for fever by adult was unsatisfactory. Our results support local nowcasting of hospitalizations on the basis of symptom data recorded in routine healthcare during the initial stage of a pandemic.

COVID-19 Pandemic and Its Impact on Training Programs of Medical Residency in Romania

Background: The COVID-19 pandemic has had a negative impact on the training process for resident physicians. The objective of this study was to evaluate the impact of the COVID-19 pandemic on professional training, and also the subjective perception of the levels of stress, anxiety, and depression among resident doctors specializing in gastroenterology in Romania. Methods: We conducted an observational cross-sectional study, for a period of two months, among 180 resident doctors specializing in gastroenterology, working in university hospitals in Romania. A questionnaire consisting of 29 questions distributed through social media platforms was completed in Google Forms. Statistical analyses were performed using IBM SPSS software v.20. Results: A linear relationship was identified between the number of daily hospitalizations in the gastroenterology department and the rate of SARS-CoV-2 infection among resident physicians. In total, 80% of the participants reported an increase in the levels of stress, anxiety, and depression during the COVID-19 pandemic, and 88.3% stated that they were unsatisfied by online courses. Conclusions: The COVID-19 pandemic has had negative effects on both professional training and levesl of stress, anxiety and depression of resident doctors specializing in gastroenterology. In the specialty of gastroenterology there may be certain peculiarities, due to the interventional aspects that this medical specialty involves, for example, endoscopic procedures. Thus, the necessity to acquire practical skills in addition to theoretical knowledge increases the negative impact on gastroenterology internship.

Ambient PM Concentrations as a Precursor of Emergency Visits for Respiratory Complaints: Roles of Deep Learning and Multi-Point Real-Time Monitoring

Despite ample evidence that high levels of particulate matter (PM) are associated with increased emergency visits related to respiratory diseases, little has been understood about how prediction processes could be improved by incorporating real-time data from multipoint monitoring stations. While previous studies use traditional statistical models, this study explored the feasibility of deep learning algorithms to improve the accuracy of predicting daily emergency hospital visits by tracking their spatiotemporal association with PM concentrations. We compared the predictive accuracy of the models based on PM datasets collected between 1 December 2019 and 31 December 2021 from a single but more accurate air monitoring station in each district (Air Korea) and multiple but less accurate monitoring sites (Korea Testing &amp; Research Institute; KTR) within Guro District in Seoul, South Korea. We used MLP (multilayer perceptron) to integrate PM data from multiple locations and then LSTM (long short-term memory) models to incorporate the intrinsic temporal PM trends into the learning process. The results reveal evidence that predictive accuracy is improved from 1.67 to 0.79 in RMSE when spatial variations of air pollutants from multi-point stations are incorporated in the algorithm as a 9-day time window. The findings suggest guidelines on how environmental and health policymakers can arrange limited resources for emergency care and design ambient air monitoring and prevention strategies.

Is Cold Apparent Temperature Associated With the Hospitalizations for Osteoporotic Fractures in the Central Areas of Wuhan? A Time-Series Study.

Osteoporosis is alarming problem due to aggravation of global aging, especially in China. Osteoporotic fracture (OF) is one of the most severe consequents of osteoporosis. Many previous studies found that environmental factors had adverse effects on human health. Cold temperature was associated with OF and bone metabolism in prior observational and experimental researches. However, few studies had been conducted on the acute effect of low temperature and OF. Data on daily meteorological factors and hospitalizations for OF were collected from Wuhan, China, between January 1, 2017 to December 24, 2019. Apparent temperature (AT), comprehensively considered a variety of environmental factors, was calculated by ambient temperature, relative humidity and wind speed. A generalized linear regression model combined with distributed lag non-linear regression model (DLNM) with quasi-Poisson link was used to explore the association between AT and the number of hospitalizations for OF. Subgroup analyses stratified by gender, age and the history of fracture were applied for detecting susceptible people. The exposure-response curve of AT and OF were generally U-shaped with lowest point at 25.8°C. The significant relationship of AT-OF existed only in cold effect (−2.0 vs. 25.8°C) while not in warm effect (37.0 vs. 25.8°C). Statistically significant risks of OF for cold effects were only found in females [RR = 1.12 (95%CI: 1.02, 1.24) at lag 2 day], aged <75 years old [RR = 1.18 (95%CI: 1.04, 1.33) and 1.17 (95%CI: 1.04, 1.33) at lag 2 and 3 days, respectively] and people with history of fracture [RR = 1.39 (95%CI: 1.02, 1.90) and 1.27 (95%CI: 1.05, 1.53) at lag 1 and 2 days, respectively]. The significant associations of AT on OF were only found in cold effect. The females, people aged <75 years and people with history of fracture possibly appeared to be more vulnerable. Public health departments should pay attention to the negative effect of cold AT and take measures in time.

Children's exposure to air pollution in a natural gas industrial area and their risk of hospital admission for respiratory diseases

The rapid expansion of the natural gas industry to meet the global demand have raised environmental health concerns. Few studies have found that areas with natural gas industrial activity have poor air quality. However, the negative health impacts of ambient air pollution in a natural gas industrial area remain unclear. This study aimed to explore the relationship between short-term exposure to air pollution and hospital admissions for respiratory diseases among children in a natural gas industrial area in Bintulu, Malaysia. Daily hospital admissions for respiratory diseases among children were collected from a hospital in Bintulu from 2010 to 2019. Data on six air pollutants (PM10, PM2.5, SO2, NO2, O3, and CO) in the study area were obtained from the Department of Environment Malaysia. Quasi-Poisson time series regressions with distributed lag non-linear models (DLNM) were applied to explore the associations between ambient air pollution and childhood hospitalisations for respiratory diseases. Stratification analyses were performed by gender and age group to identify the vulnerable populations. A 10 μg/m3 increased PM2.5 and SO2 was associated with hospital admissions for respiratory diseases among children with the greatest relative risk of RR 1.089 (95% CI 1.001–1.183) at cumulative lag 0–2 days and RR 1.229 (95% CI 1.073–1.409) at cumulative lag 0–6 days, respectively. There was no significant association between short-term exposure of PM10, NO2, CO, and O3 with childhood respiratory hospitalisation. The association between PM2.5 and SO2 exposure and hospital admissions for childhood respiratory diseases in the two pollutants model remained statistically significant. There were stronger associations in younger children aged 0–4 years and girls. This study reveals that short-term exposure to SO2 was associated with a higher risk of respiratory hospitalisations among children in Bintulu than PM2.5. Better air quality control is necessary for children's health living in the natural gas industrial area.

Comparative assessment of methods for short-term forecasts of COVID-19 hospital admissions in England at the local level

BackgroundForecasting healthcare demand is essential in epidemic settings, both to inform situational awareness and facilitate resource planning. Ideally, forecasts should be robust across time and locations. During the COVID-19 pandemic in England, it is an ongoing concern that demand for hospital care for COVID-19 patients in England will exceed available resources.MethodsWe made weekly forecasts of daily COVID-19 hospital admissions for National Health Service (NHS) Trusts in England between August 2020 and April 2021 using three disease-agnostic forecasting models: a mean ensemble of autoregressive time series models, a linear regression model with 7-day-lagged local cases as a predictor, and a scaled convolution of local cases and a delay distribution. We compared their point and probabilistic accuracy to a mean-ensemble of them all and to a simple baseline model of no change from the last day of admissions. We measured predictive performance using the weighted interval score (WIS) and considered how this changed in different scenarios (the length of the predictive horizon, the date on which the forecast was made, and by location), as well as how much admissions forecasts improved when future cases were known.ResultsAll models outperformed the baseline in the majority of scenarios. Forecasting accuracy varied by forecast date and location, depending on the trajectory of the outbreak, and all individual models had instances where they were the top- or bottom-ranked model. Forecasts produced by the mean-ensemble were both the most accurate and most consistently accurate forecasts amongst all the models considered. Forecasting accuracy was improved when using future observed, rather than forecast, cases, especially at longer forecast horizons.ConclusionsAssuming no change in current admissions is rarely better than including at least a trend. Using confirmed COVID-19 cases as a predictor can improve admissions forecasts in some scenarios, but this is variable and depends on the ability to make consistently good case forecasts. However, ensemble forecasts can make forecasts that make consistently more accurate forecasts across time and locations. Given minimal requirements on data and computation, our admissions forecasting ensemble could be used to anticipate healthcare needs in future epidemic or pandemic settings.

Ambient Particulate Air Pollution and Daily Hospital Admissions in 31 Cities in Poland

A strong and consistent association has been observed between morbidity or mortality rates and PM concentration, and is well documented in many countries. In Poland, despite poor air quality, studies concerning the evaluation of acute health effects of ambient air pollution on morbidity from respiratory or cardiovascular diseases are rare. We examined the short-term impact of PMx concentration on hospital admission in 31 Polish cities based on the daily PM10, PM2.5 concentration, meteorological variables, and hospital data. The generalized additive model (GAM) and a random-effects meta-analysis were used to assess the impact of air pollution on morbidity within the exposed population. Almost 1.6 million cardiovascular admissions and 600 thousand respiratory disorders were analyzed. The RR values for PM10-related cardiovascular and respiratory hospital admissions in Poland are equal to 1.0077 (95% confidence interval, 1.0062 to 1.0092) and 1.0218 (95% confidence interval, 1.0182 to 1.0253), respectively, while for PM2.5 1.0088 (95% confidence interval, 1.0072 to 1.0103) and 1.0289 (95% confidence interval, 1.0244 to 1.0335), respectively. Moreover, a moderate heterogeneity of RR estimates was observed between the analyzed cities (I2 values from 27% to 45%). The presented analysis confirms the significant association between hospital admission and PMx concentration, especially during heating seasons.

Validation of a deep learning algorithm identifying diagnostic quality cardiac ultrasound reference views during search phase

Abstract Funding Acknowledgements Type of funding sources: Private company. Main funding source(s): DESKi Background Transthoracic echocardiography requires long and demanding training. The use of deep learning algorithms for the identification of diagnostic quality reference views opens the way for assisting low-experienced operators. However, such algorithms have only been trained and validated on retrospective data extracted from daily hospital practice. Those retrospective data are the best clips selected by experts during each exam. Thus, the performance of those algorithms has not been evaluated on complete acquisitions, including the search phase, i.e. when the operator is moving the probe to reach the optimal position. Those search phases are not recorded in clinical practice. Purpose The objective of this study was to evaluate the performance of a deep learning algorithm that identifies diagnostic quality images for 7 echocardiographic reference views on a prospective dataset including search phases. Methods A retrospective dataset containing acquisitions extracted from daily hospital practice was created. The retrospective dataset was manually annotated by experts following quality criteria that were defined for 7 main reference views: parasternal long and short axis, apical 4, 2, and 3-chamber, subcostal 4-chamber and inferior vena cava. Each frame of the acquisitions was annotated by an expert and reviewed by a second expert. A deep learning model was trained on the retrospective dataset and evaluated with 5-fold cross-validation. In a prospective dataset, for each included patient and each reference view, operators recorded the entire acquisition, including the search phase. Acquisitions were manually annotated by 2 experts following the same quality criteria. The model trained on retrospective data was evaluated on this prospective dataset against manual experts’ annotations. Additionally, 3 experts annotated a subset of the prospective dataset to evaluate the inter-observer variability. Results 481,111 frames from 1,325 patients were annotated for the retrospective dataset. On the 5-fold cross-validation, the algorithm reached an average accuracy of 89.2 ± 0.7% per frame and an average F1-score of 87.9 ± 0.9% per frame. For the prospective dataset, 70 patients were included and 143,804 frames annotated. The average accuracy was 84.8 ± 5.0% per patient, and the average F1-score was 83.1 ± 8.7% per patient. The average inter-observer agreement rates, performed on 10 patients (23,390 frames), were between 82.8 ± 4.9% and 86.3 ± 6.2%. Conclusions We propose a deep learning algorithm that automatically identifies 7 of the most common reference views with diagnostic quality while achieving performance within the range of the inter-observer variability in acquisitions including search phases.

The rationalization of personal protective equipment usage and the solution of its limitation during COVID-19 era

Covid-19 pandemic is a global burden health problem caused by Coronavirus. The disease can be transmitted swiftly by droplets after sneezing, coughing, or holding stuff (droplet contaminated). The contagion is eased by bad habit like does not wash hand after touching others or stuff. To date, data says that The Covid-19 morbidity rate is still highest in a densely populated place. Even though several mechanisms in the community have been done to prevent the spreading of this disease, but the spread is still relatively high and needs medical treatment intervention in the health care system especially in the hospital.1 Moreover, the daily hospital treatment of Covid-19 patients’ needs appropriate personal protective equipment (PPE) to protect and to minimalize the risk of infection to health workers.

Offline: Complacency threatens progress against COVID-19

Hans Kluge, WHO's Regional Director for Europe, has been reported as saying that European nations could soon be entering a “long period of tranquillity” as the pandemic abates. Pointing out that mortality from COVID-19 seemed to be plateauing, he suggested the continent was approaching a “plausible endgame” and an “enduring peace”. As WHO's chief spokesperson for the 53 countries that make up his regional responsibility, Kluge was no doubt seeking to strike an optimistic and encouraging note. But it is disappointing that he took such a narrow geographical perspective about a global pandemic. His words promote a false reassurance that could breed complacency, even conceit. Omicron has spread across Europe from west to east, with countries on the eastern reaches of the continent still experiencing increased transmission. The Institute for Health Metrics and Evaluation (IHME) predicts that half the population of Europe will have been infected by March 1. Infections are estimated to be a staggering 10 million per day. R is greater than 1 in at least 24 countries in the region. Hospitalisations and deaths are still increasing in some areas. With over 5000 deaths each day, COVID-19 remains the second largest killer after ischaemic heart disease. Health systems will experience particular pressure in the next few weeks, not least because of shortages of health workers. Daily hospitalisations will peak at 1·1 million in early February. In the coming weeks, IHME predicts that 45 countries will endure high or extreme stress on their intensive care facilities. 174 000 additional deaths from COVID-19 are predicted between Jan 31 and June 1. This period cannot be defined as tranquil. Although the aftermath of the omicron wave will most likely usher in a relatively quiet spring and summer, IHME predicts that “COVID-19 will return”. Waning immunity and an approaching winter will create conditions for a further surge of infections later in 2022. There is no immediate endgame in sight. The available data point to a much more uncertain future. Indeed, the vaccination status of Kluge's member states reveals a sharply divided continent. Western European nations have achieved relatively high levels of vaccination. 22 countries have double vaccinated over two-thirds of their populations. But as one looks eastwards, the picture is far more bleak. 19 countries have vaccinated fewer than half of their citizens. Rather than celebrating an “enduring peace”, Kluge should be urging countries to be vigilant for new (and old) SARS-CoV-2 variants, build stronger surveillance capacities, address vaccine hesitancy, expand access to antivirals, maintain clear public health messaging about protecting those most vulnerable to the virus, and be wary of animal reservoirs that could reseed human populations with the virus. Europe is not an island. The continent exists amid an evolving global pandemic. R is greater than 1 in 71 countries. Estimated infections still stand at more than 40 million per day. Hospitalisations are around 1·9 million per day. Deaths have increased to over 17 000 per day. COVID-19 is the third largest cause of death worldwide, after heart disease and stroke. IHME predicts that 754 000 deaths from COVID-19 are expected between Jan 31 and June 1. And a huge question mark hangs over China, where the omicron surge has yet to hit. Amid these daunting statistics, new evidence is accruing about the lessons we can learn from this pandemic—lessons that should be embraced more assiduously by global health leaders. Perhaps the most striking finding came recently from the COVID-19 National Preparedness Collaborators. They found that the measures of pandemic preparedness we have relied on in the past do not explain variations in infection fatality between countries. What has been missing from understanding why some countries performed better than others was political context. Countries with low levels of trust in government suffered more. It is this political context that WHO's leaders shrink from underlining. It is far easier to point to technical weaknesses in national and global responses. But WHO should also be more vocal in holding political leaders accountable for the fractured trust their regimes have fostered. A lack of honesty in the assessments of pandemic responses by global health leaders indicates that these lessons have not been learned. And while WHO continues to avoid these inconvenient truths, the world's peoples remain at risk from political ineptitude and dysfunctional governance.