Data Model Study for Advancement of Japanese PSI Management Introducing the Concepts of S&OP

Under the impact of COVID-19, the global economy exhibits obvious slowdown. In such situation, the issues on how to keep balance between supply and demand in SCM (Supply Chain Management) operation have been more apparent than before. For SCM rebuilding, S&OP received extensive attention worldwide. However, there are few examples of successful implementation of S&OP in Japan because Japanese have not been growing accustomed to the phrase S&OP, although the challenges in SCM operations and PSI (Product, Sales, Inventory) management are recognized. Thus, no clear and exact solution is found to guide the operation. In order to further improve the current management level, a new design proposal of data model is rendered to advance the current PSI management that introduces the concepts of S&OP. Especially, we will address the motivation why we need use multidimensional database architecture to design the S&OP process instead of using RDB (Relational Database) which is often used in ERP (Enterprise Resource Planning). © 2022 IEEE.

A Multisite Assessment of Inpatient Safety Event Rates During the Coronavirus Disease 2019 Pandemic.

To date, there has been a notable lack of peer-reviewed or publicly available data documenting rates of hospital quality outcomes and patient safety events during the coronavirus disease 2019 pandemic era. The dearth of evidence is perhaps related to the US health care system triaging resources toward patient care and away from reporting and research and also reflects that data used in publicly reported hospital quality rankings and ratings typically lag 2-5 years. At our institution, a learning health system assessment is underway to evaluate how patient safety was affected by the pandemic. Here we share and discuss early findings, noting the limitations of self-reported safety event reporting, and suggest the need for further widespread investigations at other US hospitals. During the 2-year study period from January 1, 2020, through December 31, 2021 across 3 large US academic medical centers at our institution, we documented an overall rate of 25.8 safety events per 1000 inpatient days. The rate of events meeting "harm" criteria was 12.4 per 1000 inpatient days, the rate of nonharm events was 11.1 per 1000 inpatient days, and the fall rate was 2.3 per 1000 inpatient days. This descriptive exploratory analysis suggests that patient safety event rates at our institution did not increase over the course of the pandemic. However, increasing health care worker absences were nonlinearly and strongly associated with patient safety event rates, which raises questions regarding the mechanisms by which patient safety event rates may be affected by staff absences during pandemic peaks.

Adolescents with Neuropsychiatric Disorders during the COVID-19 Pandemic: Focus on Emotional Well-Being and Parental Stress.

(1) Introduction: The aim of our research was to explore emotional/behavioral changes in adolescents with neuropsychiatric conditions during the COVID-19 pandemic, and parental stress levels through a standardized assessment, comparing the data collected before and during the first months of lockdown. Moreover, an additional goal was to detect a possible relationship between emotional/behavioural symptoms of adolescents and the stress levels of their parents. (2) Methods: We enrolled 178 Italian adolescents aged between 12-18 that were referred to the Child Neuropsychiatry Unit of the University Hospital of Salerno with different neuropsychiatric diagnoses. Two standardized questionnaires were provided to all parents for the assessment of parental stress (PSI-Parenting Stress Index-Short Form) and the emotional/behavioral problems of their children (Child Behaviour Check List). The data collected from questionnaires administered during the six months preceding the pandemic, as is our usual clinical practice, were compared to those recorded during the pandemic. (3) Results: The statistical comparison of PSI and CBCL scores before/during the pandemic showed a statistically significant increase in all subscales in the total sample. The correlation analysis highlighted a significant positive relationship between Parental Stress and Internalizing/Externalizing symptoms of adolescent patients. Age and gender did not significantly affect CBCL and PSI scores, while the type of diagnosis could affect behavioral symptoms and parental stress. (4) Conclusions: our study suggests that the lockdown and the containment measures adopted during the COVID-19 pandemic could have aggravated the emotional/behavioral symptoms of adolescents with neuropsychiatric disorders and the stress of their parents. Further studies should be conducted in order to monitor the evolution of these aspects over time.

External Validation and Recalibration of the CURB-65 and PSI for Predicting 30-Day Mortality and Critical Care Intervention in Multiethnic Patients with COVID-19.

OBJECTIVES: To validate and recalibrate the CURB-65 and pneumonia severity index (PSI) in predicting 30-day mortality and critical care intervention (CCI) in a multiethnic population with COVID-19, along with evaluating both models in predicting CCI. METHODS: Retrospective data was collected for 1181 patients admitted to the largest hospital in Qatar with COVID-19 pneumonia. The area under the curve (AUC), calibration curves, and other metrics were bootstrapped to examine the performance of the models. Variables constituting the CURB-65 and PSI scores underwent further analysis using the Least Absolute Shrinkage and Selection Operator (LASSO) along with logistic regression to develop a model predicting CCI. Complex machine learning models were built for comparative analysis. RESULTS: The PSI performed better than CURB-65 in predicting 30-day mortality (AUC 0.83, 0.78 respectively), while CURB-65 outperformed PSI in predicting CCI (AUC 0.78, 0.70 respectively). The modified PSI/CURB-65 model (respiratory rate, oxygen saturation, hematocrit, age, sodium, and glucose) predicting CCI had excellent accuracy (AUC 0.823) and good calibration. CONCLUSIONS: Our study recalibrated, externally validated the PSI and CURB-65 for predicting 30-day mortality and CCI, and developed a model for predicting CCI. Our tool can potentially guide clinicians in Qatar to stratify patients with COVID-19 pneumonia.

Evaluating the ability of the NLHA2 and artificial neural network models to predict COVID-19 severity, and comparing them with the four existing scoring systems.

To improve the identification and subsequent intervention of COVID-19 patients at risk for ICU admission, we constructed COVID-19 severity prediction models using logistic regression and artificial neural network (ANN) analysis and compared them with the four existing scoring systems (PSI, CURB-65, SMARTCOP, and MuLBSTA). In this prospective multi-center study, 296 patients with COVID-19 pneumonia were enrolled and split into the General-Ward-Care group (N = 238) and the ICU-Admission group (N = 58). The PSI model (AUC = 0.861) had the best results among the existing four scoring systems, followed by SMARTCOP (AUC = 0.770), motified-MuLBSTA (AUC = 0.761), and CURB-65 (AUC = 0.712). Data from 197 patients (training set) were analyzed for modeling. The beta coefficients from logistic regression were used to develop a severity prediction model and risk score calculator. The final model (NLHA2) included five covariates (consumes alcohol, neutrophil count, lymphocyte count, hemoglobin, and AKP). The NLHA2 model (training: AUC = 0.959; testing: AUC = 0.857) had similar results to the PSI model, but with fewer variable items. ANN analysis was used to build another complex model, which had higher accuracy (training: AUC = 1.000; testing: AUC = 0.907). Discrimination and calibration were further verified through bootstrapping (2000 replicates), Hosmer-Lemeshow goodness of fit testing, and Brier score calculation. In conclusion, the PSI model is the best existing system for predicting ICU admission among COVID-19 patients, while two newly-designed models (NLHA2 and ANN) performed better than PSI, and will provide a new approach for the development of prognostic evaluation system in a novel respiratory viral epidemic.

COVID-19 Vaccine Hesitancy Among Older Adolescents and Young Adults: A National Cross-Sectional Study in China.

Background: With promotion of COVID-19 vaccinations, there has been a corresponding vaccine hesitancy, of which older adolescents and young adults represent groups of particular concern. In this report, we investigated the prevalence and reasons for vaccine hesitancy, as well as potential risk factors, within older adolescents and young adults in China. Methods: To assess these issues, an online survey was administered over the period from March 14 to April 15, 2021. Older adolescents (16-17 years old) and young adults (18-21 years old) were recruited nationwide from Wechat groups and results from a total of 2,414 respondents were analyzed. Socio-demographic variables, vaccine hesitancy, psychological distress, abnormal illness behavior, global well-being and social support were analyzed in this report. Results: Compared to young adults (n = 1,405), older adolescents (n = 1,009) showed higher prevalence rates of COVID-19 vaccine hesitancy (16.5 vs. 7.9%, p < 0.001). History of physical diseases (p = 0.007) and abnormal illness behavior (p = 0.001) were risk factors for vaccine hesitancy among older adolescents, while only a good self-reported health status (p = 0.048) was a risk factor for young adults. Concerns over COVID-19 vaccine side effects (67.1%) and beliefs of invulnerability regarding infection risk (41.9%) were the most prevalent reasons for vaccine hesitancy. Providing evidence on the vaccine reduction of COVID-19 infection risk (67.5%), ensuring vaccine safety (56.7%) and the low risk of side effects (52.7%) were the most effective persuasions for promoting vaccinations. Conclusion: In China, older adolescents showed a higher prevalence for vaccine hesitancy than that of young adults. Abnormal illness behavior and history of physical diseases were risk factors for vaccine hesitancy among these older adolescents, while social support represents an important factor which could help to alleviate this hesitancy.

Auto-ethnography and psy-critique in Covid times. A book review essay of Ian Parker’s Psychology through Critical Auto-ethnography

This book review essay of Ian Parker’s Psychology through Critical Auto-ethnography has three objectives. The first is to provide an assessment of Parker’s unique contribution to the field of Critical Psychology. Parker’s critique of the psy-sciences is shown to offer a key challenge not only to mainstream psychology but also to those who envision themselves working in the field of Critical Psychology: how not to relapse in the traps of mainstream psychology and psychologisation? The second objective is to scrutinize Parker’s idiosyncratic use of the methodology of auto-ethnography. Here it is argued, again, that Parker’s appropriation of this method not only is ideally positioned to question the problematic field of mainstream psychology, but also opens up a different perspective on subjectivity and sociality that should challenge Critical Psychology. The third objective is to apply these insights to the Covid crisis: if Parker enjoins us to step outside the psy-complex and “find many other ways to live together without it,” the entry of mainstream psychology into the Covid-debate, claiming expert knowledge on how we should live apart/together, should be confronted head-on. To achieve these three objectives, the author also uses a moderate dose of auto-ethnography.

Circuit-based PSI for Covid-19 Risk Scoring

Many solutions have been proposed to improve manual contact tracing for infectious diseases through automation. Privacy is crucial for the deployment of such a system as it greatly influences adoption. Approaches for digital contact tracing like Google Apple Exposure Notification (GAEN) protect the privacy of users by decentralizing risk scoring. But GAEN leaks information about diagnosed users as ephemeral pseudonyms are broadcast to everyone. To combat deanonymisation based on the time of encounter while providing extensive risk scoring functionality we propose to use a private set intersection (PSI) protocol based on garbled circuits. Using oblivious programmable pseudo random functions PSI (OPPRF-PSI) , we implement our solution CERTAIN which leaks no information to querying users other than one risk score for each of the last 14 days representing their risk of infection. We implement payload inclusion for OPPRF-PSI and evaluate the efficiency and performance of different risk scoring mechanisms on an Android device.

The daily urban dynamic indicator: Gauging the urban dynamic in Porto during the COVID-19 pandemic.

The SARS-CoV-2 outbreak motivated the development of a myriad of weekly and daily indicators that track economic activity to estimate and predict the consequences of the pandemic. With some exceptions, these indicators are calculated at the country level and are mainly focused on tracking economic factors, disregarding local urban phenomena. To address this, we present the Urban Dynamic Indicator (UDI), a novel composite indicator designed to measure a city's daily urban dynamic. The UDI is applied to Porto municipality, in Portugal, and it corresponds to a latent factor obtained through a factor analysis over seasonal adjusted daily data regarding traffic intensity, public transportation usage, internet usage in public buses, NO2 emissions and noise level. The UDI's values show that, by the end of 2020, despite the approach of economic activity to its pre-pandemic values, as suggested by the Portuguese Daily Economic Indicator (DEI), Porto urban dynamic did not recover completely. The UDI enriches the information available for Porto city planners and policymakers to respond to crisis situations and to gauge the application of local policies that contribute to urban sustainable planning. Furthermore, the methodology defined in this work can be followed for the development of daily urban dynamic indicators elsewhere.

CURB-65 plus hypoalbuminemia: a new score system for prediction of the in-hospital mortality risk in patients with SARS-CoV-2 pneumonia.

INTRODUCTION: There is the need of a simple but highly reliable score system for stratifying the risk of mortality and Intensive Care Unit (ICU) transfer in patients with SARS-CoV-2 pneumonia at the Emergency Room. PURPOSE: In this study, the ability of CURB-65, extended CURB-65, PSI and CALL scores and C-Reactive Protein (CRP) to predict intra-hospital mortality and ICU admission in patients with SARS-CoV-2 pneumonia were evaluated. METHODS: During March-May 2020, a retrospective, single-center study including all consecutive adult patients with diagnosis of SARS-CoV-2 pneumonia was conducted. Clinical, laboratory and radiological data as well as CURB-65, expanded CURB-65, PSI and CALL scores were calculated based on data recorded at hospital admission. RESULTS: Overall, 224 patients with documented SARS-CoV-2 pneumonia were included in the study. As for intrahospital mortality (24/224, 11%), PSI performed better than all the other tested scores, which showed lower AUC values (AUC=0.890 for PSI versus AUC=0.885, AUC=0.858 and AUC=0.743 for expanded CURB-65, CURB-65 and CALL scores, respectively). Of note, the addition of hypoalbuminemia to the CURB-65 score increased the prediction value of intra-hospital mortality (AUC=0.905). All the tested scores were less predictive for the need of ICU transfer (26/224, 12%), with the best AUC for extended CURB-65 score (AUC= 0.708). CONCLUSION: The addition of albumin level to the easy-to-calculate CURB-65 score at hospital admission is able to improve the quality of prediction of intra-hospital mortality in patients with SARS-CoV-2 pneumonia.

Pneumonia severity indices predict prognosis in coronavirus disease-2019.

BACKGROUND: Early recognition of the severe illness is critical in coronavirus disease-19 (COVID-19) to provide best care and optimize the use of limited resources. OBJECTIVES: We aimed to determine the predictive properties of common community-acquired pneumonia (CAP) severity scores and COVID-19 specific indices. METHODS: In this retrospective cohort, COVID-19 patients hospitalized in a teaching hospital between 18 March-20 May 2020 were included. Demographic, clinical, and laboratory characteristics related to severity and mortality were measured and CURB-65, PSI, A-DROP, CALL, and COVID-GRAM scores were calculated as defined previously in the literature. Progression to severe disease and in-hospital/overall mortality during the follow-up of the patients were determined from electronic records. Kaplan-Meier, log-rank test, and Cox proportional hazard regression model was used. The discrimination capability of pneumonia severity indices was evaluated by receiver-operating-characteristic (ROC) analysis. RESULTS: Two hundred ninety-eight patients were included in the study. Sixty-two patients (20.8%) presented with severe COVID-19 while thirty-one (10.4%) developed severe COVID-19 at any time from the admission. In-hospital mortality was 39 (13.1%) while the overall mortality was 44 (14.8%). The mortality in low-risk groups that were identified to manage outside the hospital was 0 in CALL Class A, 1.67% in PSI low risk, and 2.68% in CURB-65 low-risk. However, the AUCs for the mortality prediction in COVID-19 were 0.875, 0.873, 0.859, 0.855, and 0.828 for A-DROP, PSI, CURB-65, COVID-GRAM, and CALL scores respectively. The AUCs for the prediction of progression to severe disease was 0.739, 0.711, 0,697, 0.673, and 0.668 for CURB-65, CALL, PSI, COVID-GRAM, A-DROP respectively. The hazard ratios (HR) for the tested pneumonia severity indices demonstrated that A-DROP and CURB-65 scores had the strongest association with mortality, and PSI, and COVID-GRAM scores predicted mortality independent from age and comorbidity. CONCLUSION: Community-acquired pneumonia (CAP) scores can predict in COVID-19. The indices proposed specifically to COVID-19 work less than nonspecific scoring systems surprisingly. The CALL score may be used to decide outpatient management in COVID-19.

Severity Scores in COVID-19 Pneumonia: a Multicenter, Retrospective, Cohort Study.

BACKGROUND: Identification of patients on admission to hospital with coronavirus infectious disease 2019 (COVID-19) pneumonia who can develop poor outcomes has not yet been comprehensively assessed. OBJECTIVE: To compare severity scores used for community-acquired pneumonia to identify high-risk patients with COVID-19 pneumonia. DESIGN: PSI, CURB-65, qSOFA, and MuLBSTA, a new score for viral pneumonia, were calculated on admission to hospital to identify high-risk patients for in-hospital mortality, admission to an intensive care unit (ICU), or use of mechanical ventilation. Area under receiver operating characteristics curve (AUROC), sensitivity, and specificity for each score were determined and AUROC was compared among them. PARTICIPANTS: Patients with COVID-19 pneumonia included in the SEMI-COVID-19 Network. KEY RESULTS: We examined 10,238 patients with COVID-19. Mean age of patients was 66.6 years and 57.9% were males. The most common comorbidities were as follows: hypertension (49.2%), diabetes (18.8%), and chronic obstructive pulmonary disease (12.8%). Acute respiratory distress syndrome (34.7%) and acute kidney injury (13.9%) were the most common complications. In-hospital mortality was 20.9%. PSI and CURB-65 showed the highest AUROC (0.835 and 0.825, respectively). qSOFA and MuLBSTA had a lower AUROC (0.728 and 0.715, respectively). qSOFA was the most specific score (specificity 95.7%) albeit its sensitivity was only 26.2%. PSI had the highest sensitivity (84.1%) and a specificity of 72.2%. CONCLUSIONS: PSI and CURB-65, specific severity scores for pneumonia, were better than qSOFA and MuLBSTA at predicting mortality in patients with COVID-19 pneumonia. Additionally, qSOFA, the simplest score to perform, was the most specific albeit the least sensitive.

Validation of PSI/PORT, CURB-65 and SCAP scoring system in COVID-19 pneumonia for prediction of disease severity and 14-day mortality.

BACKGROUND: The unprecedented COVID-19 pandemic has put a serious burden on the healthcare system worldwide. Due to varied manifestations of SARS-CoV-2 infection, many scoring systems, which were earlier used for community acquired pneumonia (CAP) are in use to determine the disease severity and the need of ICU admissions for proper management. COVID-19 is a relatively new disease and the validity of these scoring systems in SARS-CoV-2 infection is not completely known. This study aimed to validate these scoring systems in cases of COVID-19 pneumonia in an Indian setup. The study has also tried to find the most accurate indicator of disease severity and 14-day mortality among these scoring systems. MATERIALS AND METHODS: This study included 122 SARS-CoV-2 infected patients at a tertiary hospital in Ranchi, Jharkhand. The severity of the disease according to ICMR protocol for COVID-19, the PSI/PORT score, the CURB-65 score and the SCAP score were calculated in all the patients and analysed with the disease outcome, that is, 14-day mortality. RESULTS: SCAP score, PSI/PORT score and CURB-65 criteria, all were good indicators of disease severity and 14-day mortality. However, when compared to other scoring systems, SCAP score was a more accurate marker of disease severity and 14-day mortality. CONCLUSION: The PSI/PORT scoring system, the CURB-65 criteria and the SCAP scoring system can be used to assess the COVID-19 severity and predict the 14-day mortality risk in cases of COVID-19 pneumonia.

Predicting severe COVID-19 in the Emergency Department.

BACKGROUND: COVID-19 may lead to severe disease, requiring intensive care treatment and challenging the capacity of health care systems. The aim of this study was to compare the ability of commonly used scoring systems for sepsis and pneumonia to predict severe COVID-19 in the emergency department. METHODS: Prospective, observational, single centre study in a secondary/tertiary care hospital in Oslo, Norway. Patients were assessed upon hospital admission using the following scoring systems; quick Sequential Failure Assessment (qSOFA), Systemic Inflammatory Response Syndrome criteria (SIRS), National Early Warning Score 2 (NEWS2), CURB-65 and Pneumonia Severity index (PSI). The ratio of arterial oxygen tension to inspiratory oxygen fraction (P/F-ratio) was also calculated. The area under the receiver operating characteristics curve (AUROC) for each scoring system was calculated, along with sensitivity and specificity for the most commonly used cut-offs. Severe disease was defined as death or treatment in ICU within 14 days. RESULTS: 38 of 175 study participants developed severe disease, 13 (7%) died and 29 (17%) had a stay at an intensive care unit (ICU). NEWS2 displayed an AUROC of 0.80 (95% confidence interval 0.72-0.88), CURB-65 0.75 (0.65-0.84), PSI 0.75 (0.65-0.84), SIRS 0.70 (0.61-0.80) and qSOFA 0.70 (0.61-0.79). NEWS2 was significantly better than SIRS and qSOFA in predicating severe disease, and with a cut-off of5 points, had a sensitivity and specificity of 82% and 60%, respectively. CONCLUSION: NEWS2 predicted severe COVID-19 disease more accurately than SIRS and qSOFA, but not significantly better than CURB65 and PSI. NEWS2 may be a useful screening tool in evaluating COVID-19 patients during hospital admission. TRIAL REGISTRATION: : ClinicalTrials.gov Identifier: NCT04345536. (https://clinicaltrials.gov/ct2/show/NCT04345536).