Increased time from physiological derangement to critical care admission associates with mortality.

BACKGROUND: Rapid response systems aim to achieve a timely response to the deteriorating patient; however, the existing literature varies on whether timing of escalation directly affects patient outcomes. Prior studies have been limited to using 'decision to admit' to critical care, or arrival in the emergency department as 'time zero', rather than the onset of physiological deterioration. The aim of this study is to establish if duration of abnormal physiology prior to critical care admission ['Score to Door' (STD) time] impacts on patient outcomes. METHODS: A retrospective cross-sectional analysis of data from pooled electronic medical records from a multi-site academic hospital was performed. All unplanned adult admissions to critical care from the ward with persistent physiological derangement [defined as sustained high National Early Warning Score (NEWS) > / = 7 that did not decrease below 5] were eligible for inclusion. The primary outcome was critical care mortality. Secondary outcomes were length of critical care admission and hospital mortality. The impact of STD time was adjusted for patient factors (demographics, sickness severity, frailty, and co-morbidity) and logistic factors (timing of high NEWS, and out of hours status) utilising logistic and linear regression models. RESULTS: Six hundred and thirty-two patients were included over the 4-year study period, 16.3% died in critical care. STD time demonstrated a small but significant association with critical care mortality [adjusted odds ratio of 1.02 (95% CI 1.0-1.04, p = 0.01)]. It was also associated with hospital mortality (adjusted OR 1.02, 95% CI 1.0-1.04, p = 0.026), and critical care length of stay. Each hour from onset of physiological derangement increased critical care length of stay by 1.2%. STD time was influenced by the initial NEWS, but not by logistic factors such as out-of-hours status, or pre-existing patient factors such as co-morbidity or frailty. CONCLUSION: In a strictly defined population of high NEWS patients, the time from onset of sustained physiological derangement to critical care admission was associated with increased critical care and hospital mortality. If corroborated in further studies, this cohort definition could be utilised alongside the 'Score to Door' concept as a clinical indicator within rapid response systems.

Infection precautions for severe acute respiratory syndrome coronavirus 2 in assisted reproduction centers: dodging an invisible bullet.

The coronavirus disease 2019 pandemic has resulted in many changes in how we interact in society, requiring that we protect ourselves and others from an invisible, airborne enemy called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Until a vaccine is developed, and it reaches high levels of distribution, everyone must continue to be diligent to limit the viral spread. The practice of assisted reproduction during this pandemic presents unique challenges in addition to the risks identified in general clinical care. The established good tissue practices employed in laboratories are not designed to protect gametes and embryos from an airborne virus, particularly one that may be shed by an asymptomatic staff member. Armed with theoretical risks but lacking direct evidence, assisted-reproduction teams must examine every aspect of their practice, identify areas at a risk of exposure to SARS-CoV-2, and develop a mitigation plan. Several professional fertility societies have created guidelines for the best practices in patient care during the coronavirus disease 2019 pandemic. As we learn more about SARS-CoV-2, updates have been issued to help adapt infection-control and -prevention protocols. This review discusses what is currently known about SARS-CoV-2 infection risks in assisted reproductive centers and recommends the implementation of specific mitigation strategies.

Biosafety during a pandemic: shared resource laboratories rise to the challenge.

Biosafety has always been an important aspect of daily work in any research institution, particularly for cytometry Shared Resources Laboratories (SRLs). SRLs are common-use spaces that facilitate the sharing of knowledge, expertise, and ideas. This sharing inescapably involves contact and interaction of all those within this working environment on a daily basis. The current pandemic caused by SARS-CoV-2 has prompted the re-evaluation of many policies governing the operations of SRLs. Here we identify and review the unique challenges SRLs face in maintaining biosafety standards, highlighting the potential risks associated with not only cytometry instrumentation and samples, but also the people working with them. We propose possible solutions to safety issues raised by the COVID-19 pandemic and provide tools for facilities to adapt to evolving guidelines and future challenges.

COVID-19 Inmate Risk Appraisal (CIRA): development and validation of a screening tool to assess COVID-19 vulnerability in prisons.

OBJECTIVES: To develop and validate a screening tool designed to identify detained people at increased risk for COVID-19 mortality, the COVID-19 Inmate Risk Appraisal (CIRA). DESIGN: Cross-sectional study with a representative sample (development) and a case-control sample (validation). SETTING: The two largest Swiss prisons. PARTICIPANTS: (1) Development sample: all male persons detained in Pöschwies, Zurich (n = 365); (2) Validation sample: case-control sample of male persons detained in Champ-Dollon, Geneva (n = 192, matching 1:3 for participants at risk for severe course of COVID-19 and participants without risk factors). MAIN OUTCOME MEASURES: The CIRA combined seven risk factors identified by the World Health Organization and the Swiss Federal Office of Public Health as predictive of severe COVID-19 to derive an absolute risk increase in mortality rate: Age ≥60 years, cardiovascular disease, diabetes, hypertension, chronic respiratory disease, immunodeficiency and cancer. RESULTS: Based on the development sample, we proposed a three-level classification: average (<3.7), elevated (3.7-5.7) and high (>5.7) risk. In the validation sample, the CIRA identified all individuals identified as vulnerable by national recommendations (having at least one risk factor). The category “elevated risk” maximised sensitivity (1) and specificity (0.97). The CIRA had even higher capacity in discriminating individuals vulnerable according to clinical evaluation (a four-level risk categorisation based on a consensus of medical staff). The category “elevated risk” maximised sensitivity and specificity (both 1). When considering the individuals classified as extremely high risk by medical staff, the category “high risk” had a high discriminatory capacity (sensitivity =0.89, specificity =0.97). CONCLUSIONS: The CIRA scores have a high discriminative ability and will be important in custodial settings to support decisions and prioritise actions using a standardised valid assessment method. However, as knowledge on risk factors for COVID-19 mortality is still limited, the CIRA may be considered preliminary. Underlying data will be updated regularly on the website (http://www.prison-research.com), where the CIRA algorithm is freely available.

Development and validation of a knowledge-driven risk calculator for critical illness in COVID-19 patients.

Facing the novel coronavirus disease (COVID-19) pandemic, evidence to inform decision-making at all care levels is essential. Based on the results of a study by Petrilli et al., we have developed a calculator using patient data at admission to predict critical illness (intensive care, mechanical ventilation, hospice care, or death). We report a retrospective validation of the calculator on 145 consecutive patients admitted with COVID-19 to a single hospital in Israel. Despite considerable differences between the original and validation study populations, of 18 patients with critical illness, 17 were correctly identified (sensitivity: 94.4%, 95% CI, 72.7%-99.9%; specificity: 81.9%, 95% CI, 74.1%-88.2%). Of 127 patients with non-critical illness, 104 were correctly identified. Our results indicate that published knowledge can be reliably applied to assess patient risk, potentially reducing the cognitive burden on physicians, and helping policymakers better prepare for future needs.

Development and validation of a laboratory risk score for the early prediction of COVID-19 severity and in-hospital mortality.

BACKGROUND AND AIMS: Coronavirus Disease 2019 is characterized by a spectrum of clinical severity. This study aimed to develop a laboratory score system to identify high-risk individuals, to validate this score in a separate cohort, and to test its accuracy in the prediction of in-hospital mortality. METHODS: In this cohort study, biological data from 330 SARS-CoV-2 infected patients were used to develop a risk score to predict progression toward severity. In a second stage, data from 240 additional COVID-19 patients were used to validate this score. Accuracy of the score was measured by the area under the receiver operating characteristic curve (AUC). RESULTS: In the development cohort, a step-wise decrease in the average survival duration was noted with the increment of the risk score (pANOVA < 0.0001). A similar trend was confirmed when analyzing this association in the validation cohort (p < 0.0001). The AUC was 0.74 [0.66-0.82] and 0.90 [0.87-0.94], p < 0.0001, respectively for severity and mortality prediction. CONCLUSION: This study provides a useful risk score based on biological routine parameters assessed at the time of admission, which has proven its effectiveness in predicting both severity and short-term mortality of COVID-19. Improved predictive scores may be generated by including other clinical and radiological features.

Data-Driven Reopening of Urban Public Education Through Chicago's Tracking of COVID-19 School Transmission.

Reopening in-person education in public schools during the coronavirus 2019 (COVID-19) pandemic requires careful risk-benefit analysis, with no current established metrics. Equity concerns in urban public schools such as decreased enrollment among largely Black and Latinx prekindergarten and special needs public school students already disproportionately impacted by the pandemic itself have added urgency to Chicago Department of Public Health's analysis of COVID-19 transmission. Close tracking within a large school system revealed a lower attack rate for students and staff participating in in-person learning than for the community overall. By combining local data from a large urban private school system with national and international data on maintaining in-person learning during COVID-19 surges, Chicago believes in-person public education poses a low risk of transmission when the operational burden imposed by the second wave has subsided.

Periprocedural complications in patients with SARS-CoV-2 infection compared to those without infection: A nationwide propensity-matched analysis.

BACKGROUND: Reports on emergency surgery performed soon after a COVID-19 infection that are not controlled for premorbid risk-factors show increased 30-day mortality and pulmonary complications. This contributed to a virtual cessation of elective surgery during the pandemic surge. To inform evidence-based guidance on the decisions for surgery during the recovery phase of the pandemic, we compare 30-day outcomes in patients testing positive for COVID-19 before their operation, to contemporary propensity-matched COVID-19 negative patients undergoing the same procedures. METHODS: This prospective multicentre study included all patients undergoing surgery at 170 Veterans Health Administration (VA) hospitals across the United States. COVID-19 positive patients were propensity matched to COVID-19 negative patients on demographic and procedural factors. We compared 30-day outcomes between COVID-19 positive and negative patients, and the effect of time from testing positive to the date of procedure (≤10 days, 11-30 days and >30 days) on outcomes. RESULTS: Between March 1 and August 15, 2020, 449 COVID-19 positive and 51,238 negative patients met inclusion criteria. Propensity matching yielded 432 COVID-19 positive and 1256 negative patients among whom half underwent elective surgery. Infected patients had longer hospital stays (median seven days), higher rates of pneumonia (20.6%), ventilator requirement (7.6%), acute respiratory distress syndrome (ARDS, 17.1%), septic shock (13.7%), and ischemic stroke (5.8%), while mortality, reoperations and readmissions were not significantly different. Higher odds for ventilation and stroke persisted even when surgery was delayed 11-30 days, and for pneumonia, ARDS, and septic shock >30 days after a positive test. DISCUSSION: 30-day pulmonary, septic, and ischaemic complications are increased in COVID-19 positive, compared to propensity score matched negative patients. Odds for several complications persist despite a delay beyond ten days after testing positive. Individualized risk-stratification by pulmonary and atherosclerotic comorbidities should be considered when making decisions for delaying surgery in infected patients.

Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation.

BACKGROUND: COVID-19 has infected millions of people worldwide and is responsible for several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods to meet these needs are lacking. OBJECTIVE: The aims of this study were to analyze the electronic health records (EHRs) of patients who tested positive for COVID-19 and were admitted to hospitals in the Mount Sinai Health System in New York City; to develop machine learning models for making predictions about the hospital course of the patients over clinically meaningful time horizons based on patient characteristics at admission; and to assess the performance of these models at multiple hospitals and time points. METHODS: We used Extreme Gradient Boosting (XGBoost) and baseline comparator models to predict in-hospital mortality and critical events at time windows of 3, 5, 7, and 10 days from admission. Our study population included harmonized EHR data from five hospitals in New York City for 4098 COVID-19-positive patients admitted from March 15 to May 22, 2020. The models were first trained on patients from a single hospital (n=1514) before or on May 1, externally validated on patients from four other hospitals (n=2201) before or on May 1, and prospectively validated on all patients after May 1 (n=383). Finally, we established model interpretability to identify and rank variables that drive model predictions. RESULTS: Upon cross-validation, the XGBoost classifier outperformed baseline models, with an area under the receiver operating characteristic curve (AUC-ROC) for mortality of 0.89 at 3 days, 0.85 at 5 and 7 days, and 0.84 at 10 days. XGBoost also performed well for critical event prediction, with an AUC-ROC of 0.80 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. In external validation, XGBoost achieved an AUC-ROC of 0.88 at 3 days, 0.86 at 5 days, 0.86 at 7 days, and 0.84 at 10 days for mortality prediction. Similarly, the unimputed XGBoost model achieved an AUC-ROC of 0.78 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. Trends in performance on prospective validation sets were similar. At 7 days, acute kidney injury on admission, elevated LDH, tachypnea, and hyperglycemia were the strongest drivers of critical event prediction, while higher age, anion gap, and C-reactive protein were the strongest drivers of mortality prediction. CONCLUSIONS: We externally and prospectively trained and validated machine learning models for mortality and critical events for patients with COVID-19 at different time horizons. These models identified at-risk patients and uncovered underlying relationships that predicted outcomes.

Derivation and Internal Validation of a Model to Predict the Probability of Severe Acute Respiratory Syndrome Coronavirus-2 Infection in Community People.

BACKGROUND: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes COVID-19 disease. There are concerns regarding limited testing capacity and the exclusion of cases from unproven screening criteria. Knowing COVID-19 risks can inform testing. This study derived and assessed a model to predict risk of SARS-CoV-2 in community-based people. METHODS: All people presenting to a community-based COVID-19 screening center answered questions regarding symptoms, possible exposure, travel, and occupation. These data were anonymously linked to SARS-CoV-2 testing results. Logistic regression was used to derive a model to predict SARS-CoV-2 infection. Bootstrap sampling evaluated the model. RESULTS: A total of 9172 consecutive people were studied. Overall infection rate was 6.2% but this varied during the study period. SARS-CoV-2 infection likelihood was primarily influenced by contact with a COVID-19 case, fever symptoms, and recent case detection rates. Internal validation found that the SARS-CoV-2 Risk Prediction Score (SCRiPS) performed well with good discrimination (c-statistic 0.736, 95%CI 0.715-0.757) and very good calibration (integrated calibration index 0.0083, 95%CI 0.0048-0.0131). Focusing testing on people whose expected SARS-CoV-2 risk equaled or exceeded the recent case detection rate would increase the number of identified SARS-CoV-2 cases by 63.1% (95%CI 54.5-72.3). CONCLUSION: The SCRiPS model accurately estimates the risk of SARS-CoV-2 infection in community-based people undergoing testing. Using SCRiPS can importantly increase SARS-CoV-2 infection identification when testing capacity is limited.

A Critical Appraisal of the American College of Surgeons Medically Necessary, Time Sensitive Procedures (MeNTS) Scoring System, Urology Consensus Recommendations and Individual Surgeon Case Prioritization for Resumption of Elective Urological Surgery During the COVID-19 Pandemic.

PURPOSE: Resumption of elective urology cases postponed due to the COVID-19 pandemic requires a systematic approach to case prioritization, which may be based on detailed cross-specialty questionnaires, specialty specific published expert opinion or by individual (operating) surgeon review. We evaluated whether each of these systems effectively stratifies cases and for agreement between approaches in order to inform departmental policy. MATERIALS AND METHODS: We evaluated triage of elective cases postponed within our department due to the COVID-19 pandemic (March 9, 2020 to May 22, 2020) using questionnaire based surgical prioritization (American College of Surgeons Medically Necessary, Time Sensitive Procedures [MeNTS] instrument), consensus/expert opinion based surgical prioritization (based on published urological recommendations) and individual surgeon based surgical prioritization scoring (developed and managed within our department). Lower scores represented greater urgency. MeNTS scores were compared across consensus/expert opinion based surgical prioritization and individual surgeon based surgical prioritization scores. RESULTS: A total of 204 cases were evaluated. Median MeNTS score was 50 (IQR 44, 55), and mean consensus/expert opinion based surgical prioritization and individual surgeon based surgical prioritization scores were 2.6±0.6 and 2.2±0.8, respectively. Median MeNTS scores were 52 (46.5, 57.5), 50 (44.5, 54.5) and 48 (43.5, 54) for individual surgeon based surgical prioritization priority 1, 2 and 3 cases (p=0.129), and 55 (51.5, 57), 47.5 (42, 56) and 49 (44, 54) for consensus/expert opinion based surgical prioritization priority scores 1, 2, and 3 (p=0.002). There was none to slight agreement between consensus/expert opinion based surgical prioritization and individual surgeon based surgical prioritization scores (Kappa 0.131, p=0.002). CONCLUSIONS: Questionnaire based, expert opinion based and individual surgeon based approaches to case prioritization result in significantly different case prioritization. Questionnaire based surgical prioritization did not meaningfully stratify urological cases, and consensus/expert opinion based surgical prioritization and individual surgeon based surgical prioritization frequently disagreed. The strengths and weaknesses of each of these systems should be considered in future disaster planning scenarios.

Living risk prediction algorithm (QCOVID) for risk of hospital admission and mortality from coronavirus 19 in adults: national derivation and validation cohort study.

OBJECTIVE: To derive and validate a risk prediction algorithm to estimate hospital admission and mortality outcomes from coronavirus disease 2019 (covid-19) in adults. DESIGN: Population based cohort study. SETTING AND PARTICIPANTS: QResearch database, comprising 1205 general practices in England with linkage to covid-19 test results, Hospital Episode Statistics, and death registry data. 6.08 million adults aged 19-100 years were included in the derivation dataset and 2.17 million in the validation dataset. The derivation and first validation cohort period was 24 January 2020 to 30 April 2020. The second temporal validation cohort covered the period 1 May 2020 to 30 June 2020. MAIN OUTCOME MEASURES: The primary outcome was time to death from covid-19, defined as death due to confirmed or suspected covid-19 as per the death certification or death occurring in a person with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the period 24 January to 30 April 2020. The secondary outcome was time to hospital admission with confirmed SARS-CoV-2 infection. Models were fitted in the derivation cohort to derive risk equations using a range of predictor variables. Performance, including measures of discrimination and calibration, was evaluated in each validation time period. RESULTS: 4384 deaths from covid-19 occurred in the derivation cohort during follow-up and 1722 in the first validation cohort period and 621 in the second validation cohort period. The final risk algorithms included age, ethnicity, deprivation, body mass index, and a range of comorbidities. The algorithm had good calibration in the first validation cohort. For deaths from covid-19 in men, it explained 73.1% (95% confidence interval 71.9% to 74.3%) of the variation in time to death (R2); the D statistic was 3.37 (95% confidence interval 3.27 to 3.47), and Harrell's C was 0.928 (0.919 to 0.938). Similar results were obtained for women, for both outcomes, and in both time periods. In the top 5% of patients with the highest predicted risks of death, the sensitivity for identifying deaths within 97 days was 75.7%. People in the top 20% of predicted risk of death accounted for 94% of all deaths from covid-19. CONCLUSION: The QCOVID population based risk algorithm performed well, showing very high levels of discrimination for deaths and hospital admissions due to covid-19. The absolute risks presented, however, will change over time in line with the prevailing SARS-C0V-2 infection rate and the extent of social distancing measures in place, so they should be interpreted with caution. The model can be recalibrated for different time periods, however, and has the potential to be dynamically updated as the pandemic evolves.

Early prediction of mortality risk among patients with severe COVID-19, using machine learning.

BACKGROUND: Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 infection, has been spreading globally. We aimed to develop a clinical model to predict the outcome of patients with severe COVID-19 infection early. METHODS: Demographic, clinical and first laboratory findings after admission of 183 patients with severe COVID-19 infection (115 survivors and 68 non-survivors from the Sino-French New City Branch of Tongji Hospital, Wuhan) were used to develop the predictive models. Machine learning approaches were used to select the features and predict the patients' outcomes. The area under the receiver operating characteristic curve (AUROC) was applied to compare the models' performance. A total of 64 with severe COVID-19 infection from the Optical Valley Branch of Tongji Hospital, Wuhan, were used to externally validate the final predictive model. RESULTS: The baseline characteristics and laboratory tests were significantly different between the survivors and non-survivors. Four variables (age, high-sensitivity C-reactive protein level, lymphocyte count and d-dimer level) were selected by all five models. Given the similar performance among the models, the logistic regression model was selected as the final predictive model because of its simplicity and interpretability. The AUROCs of the external validation sets were 0.881. The sensitivity and specificity were 0.839 and 0.794 for the validation set, when using a probability of death of 50% as the cutoff. Risk score based on the selected variables can be used to assess the mortality risk. The predictive model is available at [https://phenomics.fudan.edu.cn/risk_scores/]. CONCLUSIONS: Age, high-sensitivity C-reactive protein level, lymphocyte count and d-dimer level of COVID-19 patients at admission are informative for the patients' outcomes.