Parsimonious Risk Research Articles

WP5.13 - National Emergency Laparotomy (NELA) critical care admissions: A cohort study

Abstract Aims NELA is committed to enhancing quality of peri-operative care of patients undergoing emergency laparotomy. NELA emphasizes importance of identifying high risk patients, who are associated with significant morbidity & mortality. Critical care allows close monitoring, organ (basic/advanced) support. Aim of study is to review the NELA critical care patients. Methods Cohort study of NELA patients that received post-operative ITU care (direct/indirect admission) at single centre. Descriptive demography, and outcomes were evaluated. NELA parsimonious risk & CCMDS ITU scores used. NELA patient that did not require ITU care were controls. Results NELA critical care (n = 400) NELA non critical care (n = 466) Mann Whitney U (p value) Deaths (n) - (n) Age(yrs) 71.8 64.0 < 0.0001 Sex(M:F) 173 : 227 220 : 246 NS ASA 3 2 < 0.0001 BMI(kg/m2) 26.3 25.0 NS Lap(n) 67 168 Open(n) 333 293 Op time(mins) 131.0 109.4 < 0.0001 LOS(days) 18 9.5 < 0.0001 Deaths 30 day 50 25 60 day 66 37 90 day 73 40 Lactate(mmol/L) 2.4 1.8 <0.01 WCC(x109/L) 13.0 11.8 <0.05 CRP(mg/L) 90.1 56.6 <0.0001 Physiological-score 25.6 19.8 <0.0001 Operative-severity-score 17.9 15.9 <0.0001 Prepossum-score 21.6 7.5 <0.0001 PPmortality-score 11.9 4.9 <0.0001 PPmorbidity-score 77.3 55.7 <0.0001 Survival Rate (%) 1styear 75.4 86.2 5thyear 53.8 70.8 Logrank 10thyear 41.1 58.7 p<0.0001 Conclusion NELA critical care patients had poorer survival rates in our study. Theatre return or indirect critical care transfers can adversely affect survival. These patients were older, frail, multiple co-morbid status. NELA parsimonious mortality risk score >5% should encourage consultant level supervision, mortality risk score >10% should encourage direct critical care involvement.

Contemporary Predictors of Major Adverse Cardiovascular Events Following Percutaneous Coronary Intervention: A Nationally Representative US Sample.

Background: Patient outcomes after percutaneous coronary intervention (PCI) have improved over the last 30 years due to better techniques, therapies, and care processes. This study evaluated contemporary predictors of post-PCI major adverse cardiovascular events (MACE) and summarized risk in a parsimonious risk prediction model. Methods: The Cardiovascular Patient-Level Analytical Platform (CLiPPeR) is an observational dataset of baseline variables and longitudinal outcomes from the American College of Cardiology's CathPCI Registry® and national claims data. Cox regression was used to evaluate 2-6 years of patient follow-up (mean: 2.56 years), ending in December 2017, after index PCI between 2012 and 2015 (N = 1,450,787), to examine clinical and procedural predictors of MACE (first myocardial infarction, stroke, repeat PCI, coronary artery bypass grafting, and mortality). Cox analyses of post-PCI MACE were landmarked 28 days after index PCI. Results: Overall, 12.4% (n = 179,849) experienced MACE. All variables predicted MACE, with cardiogenic shock, cardiac arrest, four diseased coronary vessels, and chronic kidney disease having hazard ratios (HRs) ≥ 1.50. Other major predictors of MACE were in-hospital stroke, three-vessel disease, anemia, heart failure, and STEMI presentation. The index revascularization and discharge prescription of aspirin, P2Y12 inhibitor, and lipid-lowering medication had HR ≤ 0.67. The primary Cox model had c-statistic c = 0.761 for MACE versus c = 0.701 for the parsimonious model and c = 0.752 for the parsimonious model plus treatment variables. Conclusions: In a nationally representative US sample of post-PCI patients, predictors of longitudinal MACE risk were identified, and a parsimonious model efficiently encapsulated them. These findings may aid in assessing care processes to further improve care post-PCI outcomes.

Design and Adoption of a FHIR IG to Support a Telemonitoring Environment in Gatekeeper Project.

Telemedicine can provide benefits in patient affected by chronic diseases or elderly citizens as part of standard routine care supported by digital health. The GATEKEEPER (GK) Project was financed to create a vendor independent platform to be adopted in medical practice and to demonstrate its effect, benefit value, and scalability in 8 connected medical use cases with some independent pilots. This paper, after a description of the GK platform architecture, is focused on the creation of a FHIR (Fast Healthcare Interoperability Resource) IG (Implementation Guide) and its adoption in specific use cases. The final aim is to combine conventional data, collected in the hospital, with unconventional data, coming from wearable devices, to exploit artificial intelligence (AI) models designed to evaluate the effectiveness of a new parsimonious risk prediction model for Type 2 diabetes (T2D).

Clinical Risk Scores to Predict Nonsusceptibility to Trimethoprim-Sulfamethoxazole, Fluoroquinolone, Nitrofurantoin, and Third-Generation Cephalosporin Among Adult Outpatient Episodes of Complicated Urinary Tract Infection.

Clinical risk scores were developed to estimate the risk of adult outpatients having a complicated urinary tract infection (cUTI) that was nonsusceptible to trimethoprim-sulfamethoxazole (TMP-SMX), fluoroquinolone, nitrofurantoin, or third-generation cephalosporin (3-GC) based on variables available on clinical presentation. A retrospective cohort study (1 December 2017-31 December 2020) was performed among adult members of Kaiser Permanente Southern California with an outpatient cUTI. Separate risk scores were developed for TMP-SMX, fluoroquinolone, nitrofurantoin, and 3-GC. The models were translated into risk scores to quantify the likelihood of nonsusceptibility based on the presence of final model covariates in a given cUTI outpatient. A total of 30 450 cUTIs (26 326 patients) met the study criteria. Rates of nonsusceptibility to TMP-SMX, fluoroquinolone, nitrofurantoin, and 3-GC were 37%, 20%, 27%, and 24%, respectively. Receipt of prior antibiotics was the most important predictor across all models. The risk of nonsusceptibility in the TMP-SMX model exceeded 20% in the absence of any risk factors, suggesting that empiric use of TMP-SMX may not be advisable. For fluoroquinolone, nitrofurantoin, and 3-GC, clinical risk scores of 10, 7, and 11 predicted a ≥20% estimated probability of nonsusceptibility in the models that included cumulative number of prior antibiotics at model entry. This finding suggests that caution should be used when considering these agents empirically in patients who have several risk factors present in a given model at presentation. We developed high-performing parsimonious risk scores to facilitate empiric treatment selection for adult outpatients with cUTIs in the critical period between infection presentation and availability of susceptibility results.

Protocol for the derivation and external validation of a 30-day postoperative pulmonary complications (PPCs) risk prediction model for elderly patients undergoing thoracic surgery: a cohort study in southern China

IntroductionPostoperative pulmonary complications (PPCs) occur after up to 60% of non-cardiac thoracic surgery (NCTS), especially for multimorbid elderly patients. Nevertheless, current risk prediction models for PPCs have major limitations regarding...

Derivation and Validation of a Clinical Risk Assessment Model for Cancer-Associated Thrombosis in Two Unique US Health Care Systems.

Venous thromboembolism (VTE), especially pulmonary embolism (PE) and lower extremity deep vein thrombosis (LE-DVT), is a serious and potentially preventable complication for patients with cancer undergoing systemic therapy. Using retrospective data from patients diagnosed with incident cancer from 2011-2020, we derived a parsimonious risk assessment model (RAM) using least absolute shrinkage and selection operator regression from the Harris Health System (HHS, n = 9,769) and externally validated it using the Veterans Affairs (VA) health care system (n = 79,517). Bootstrapped c statistics and calibration curves were used to assess external model discrimination and fit. Dichotomized risk strata using integer scores were created and compared against the Khorana score (KS). Incident VTE and PE/LE-DVT at 6 months occurred in 590 (6.2%) and 437 (4.6%) patients in HHS and 4,027 (5.1%) and 3,331 (4.2%) patients in the VA health care system. Assessed at the time of systemic therapy initiation, the new RAM included components of the KS with the modified cancer subtype, cancer staging, systemic therapy class, history of VTE, history of paralysis/immobility, recent hospitalization, and Asian/Pacific Islander race. The c statistic was 0.71 in HHS and 0.68 in the VA health care system (compared with 0.65 and 0.60, respectively, for KS). Furthermore, the new RAM appropriately reclassified 28% of patients and increased the proportion of VTEs in the high-risk group from 37% to 68% in the validation data set. The novel RAM stratified patients with cancer into a high-risk group with 8%-10% cumulative incidence of VTE and 7% PE/LE-DVT at 6 months (v 3% and 2%, respectively, in the low-risk group). The model had improved performance over the original KS and doubled the number of VTE events in the high-risk stratum. We encourage additional external validation from prospective studies.[Media: see text].

A risk model for decline in health status after acute myocardial infarction among older adults.

Health status is increasingly recognized as an important patient-centered outcome after acute myocardial infarction (AMI). Yet drivers of decline in health status after AMI remain largely unknown in older adults. We sought to develop and validate a predictive risk model for health status decline among older adult survivors of AMI. Using data from a prospective cohort study conducted from 2013 to 2017 of 3041 patients age ≥75 years hospitalized with acute myocardial infarction at 94 U.S. hospitals, we examined a broad array of demographic, clinical, functional, and psychosocial variables for their association with health status decline, defined as a decrease of ≥5 points in the Short Form-12 (SF-12) physical component score from hospitalization to 6 months post-discharge. Model selection was performed in logistic regression models of 20 imputed datasets to yield a parsimonious risk prediction model. Model discrimination and calibration were evaluated using c-statistics and calibration plots, respectively. Of the 2571 participants included in the main analyses, 30% of patients experienced health status decline from hospitalization to 6 months post-discharge. The risk model contained 14 factors, 10 associated with higher risk of health status decline (age, pre-existing AMI, pre-existing cancer, pre-existing COPD, pre-existing diabetes, history of falls, presenting Killip class, acute kidney injury, baseline health status, and mobility impairment) and four associated with lower risk of health status decline (male sex, higher hemoglobin, receipt of revascularization, and arrhythmia during hospitalization). The model displayed good discrimination (c-statistic=0.74 in validation cohort) and calibration (p > 0.05) in both development and validation cohorts. We used split sampling to develop and validate a risk model for health status decline in older adults after hospitalization for AMI and identified several risk factors that may be modifiable to mitigate the threat of this important patient-centered outcome. External validation of this risk model is warranted.

External validation of the age, creatinine and ejection fraction II score for operative mortality prediction in patients undergoing coronary artery bypass graft surgery

Abstract Introduction Age, creatinine, and ejection fraction (ACEF) score is a parsimonious risk model to predict operative mortality in elective cardiac surgery patients. In 2017, the ACEF score was updated to the ACEF II score by adding pre-operative anemia and emergency surgery. The ACEF II score was well-calibrated and had excellent discrimination similar to the euroSCORE II. The score has never been validated in Asian patients. Therefore, we aimed to externally validate the ACEF II score in patients undergoing coronary artery bypass graft in our hospital. Method We retrospectively studied patients who underwent CABG in our hospital. Patients who received non-coronary cardiac surgery during CABG were included in the study. Patients who had missing variables in the ACEF II score calculation were excluded. Due to low number of emergency CABG in our center, urgent surgery was used instead of emergency surgery in the score calculation. Urgent surgery was defined as an operation required during the admission for a reason other than elective surgery. For instance, if CABG was performed during the index admission for MI, the procedure was counted as urgent. The primary outcome was operative mortality (in-hospital mortality or mortality within 30 days after surgery). Discrimination and calibration were assessed. The predictive performance of the ACEF II score was compared with the ACEF score. The reclassification index and clinical utility of the two scores were assessed. Results Of 703 patients included in the study, 182 were female (25.9%), 32% had diabetes with a mean age of 63.8 years. Median creatinine was 1.43 mg/dL, while the median left ventricular ejection fraction was 51.7%. The proportion of urgent CABG was 12.7%. Isolated CABG was performed in 542 patients. The operative mortality was 5.4%. The predicted probability of operative mortality by the ACEF and the ACEF II score was 4.6% and 8.3%, respectively. Calibration-in-the-large of the ACEF score was 0.19 (95% CI −0.16 to 0.55) while it was −0.62 (95% CI −0.99 to −0.24) for the ACEF II score. The ACEF and ACEF II scores had calibration slope of 0.20 (95% CI −0.01 to 0.42) and 0.33 (95% CI 0.13 to 0.53), respectively. After logistic recalibration, the agreement between actual and predicted mortality from the ACEF II score was better than the ACEF score. C-statistic of the ACEF and the ACEF II score was 0.63 (95% CI 0.54–0.71) and 0.63 (95% CI 0.53–0.72), respectively. Compared with the ACEF score, the net reclassification improvement of the ACEF II score was 8.87% [95% CI −0.95 to 18.7%], p-value: 0.0767. The integrated discrimination index of the ACEF II score was higher than the ACEF score (7.46%, 95% CI 0.77–14.16%, p-value 0.0290). Decision curve analysis showed that the clinical utility of both scores was similar. Conclusion Although the discrimination and calibration of the ACEF II score were better than the ACEF score, clinical utility of both scores was similar. Funding Acknowledgement Type of funding sources: None.

Parsimonious risk model for predicting mortality after surgical lung biopsy for interstitial lung disease.

To develop a risk model for predicting postoperative mortality and morbidity in patients with interstitial lung disease undergoing surgical lung biopsy. From 2004 to 2019, patients who underwent surgical lung biopsy for interstitial lung disease were included in this study. Based on the findings of the multivariable analysis using preoperative clinical variables, a risk model for predicting postoperative mortality and morbidity was developed. During the study period, 1177 patients were enrolled. Among them, morbidity and mortality occurred in 45 (3.8%) and 29 (2.5%) patients, respectively, which gradually declined over time from 8.9% in 2004-2005 to 0% in 2018-2019. In the final multivariable analysis, the dyspnoea grade, a forced vital capacity of ≤60%, preoperative oxygen therapy and preoperative intensive care unit stay were found to be the independent factors associated with both morbidity and mortality; smoking >40 pack-years was additionally identified as a factor related to mortality. Diffusing capacity of carbon monoxide ≤50%, which was a significant factor in the univariable analysis, became insignificant after adjustment for the forced vital capacity in the multivariable analysis. The risk scoring system based on this model showed a good discriminant ability for both morbidity [area under the receiver operating characteristic curve (95% confidence interval): 0.830 (0.726-0.932)] and mortality [0.887 (0.804-0.975)]. We developed a scoring system for predicting the risk of morbidity and mortality, which could help determine surgical candidates for lung biopsy among patients with interstitial lung disease.

PD17-01 DERIVATION AND EXTERNAL VALIDATION OF A RAPID RISK SCORE FOR PREDICTING CLINICALLY SIGNIFICANT PROSTATE CANCER IN MEN WITH AN MRI VISIBLE LESION: A MULTINATIONAL COHORT STUDY

PD17-01 DERIVATION AND EXTERNAL VALIDATION OF A RAPID RISK SCORE FOR PREDICTING CLINICALLY SIGNIFICANT PROSTATE CANCER IN MEN WITH AN MRI VISIBLE LESION: A MULTINATIONAL COHORT STUDY

Prediction of Early Periprosthetic Joint Infection After Total Hip Arthroplasty.

PurposeTo develop a parsimonious risk prediction model for periprosthetic joint infection (PJI) within 90 days after total hip arthroplasty (THA).Patients and MethodsWe used logistic LASSO regression with bootstrap ranking to develop a risk prediction model for PJI within 90 days based on a Swedish cohort of 88,830 patients with elective THA 2008–2015. The model was externally validated on a Danish cohort with 18,854 patients.ResultsIncidence of PJI was 2.45% in Sweden and 2.17% in Denmark. A model with the underlying diagnosis for THA, body mass index (BMI), American Society for Anesthesiologists (ASA) class, sex, age, and the presence of five defined comorbidities had an area under the curve (AUC) of 0.68 (95% CI: 0.66 to 0.69) in Sweden and 0.66 (95% CI: 0.64 to 0.69) in Denmark. This was superior to traditional models based on ASA class, Charlson, Elixhauser, or the Rx Risk V comorbidity indices. Internal calibration was good for predicted probabilities up to 10%.ConclusionA new PJI prediction model based on easily accessible data available before THA was developed and externally validated. The model had superior discriminatory ability compared to ASA class alone or more complex comorbidity indices and had good calibration. We provide a web-based calculator (https://erikbulow.shinyapps.io/thamortpred/) to facilitate shared decision making by patients and surgeons.

Shock Index in COVID Era

AbstractThe health care burden and risks to health care workers imposed by novel coronavirus disease 2019 (COVID-19) mandated the need for a simple, noninvasive, objective, and parsimonious risk stratification system predicting the level of care, need for definitive airway, and titration of the ongoing patient care. Shock index (SI = heart rate/systolic blood pressure) has been evaluated in emergency triage, sepsis, and trauma settings including different age group of patients. The ever accumulating girth of evidences demonstrated a superior predictive value of SI over other hemodynamic parameters. Inclusion of respiratory and/or neurological parameters and adjustment of the cutoffs appropriate to patient age increase the predictability in the trauma and sepsis scenario. Being reproducible, dynamic, and simple, SI can be a valuable patient risk stratification tool in this ongoing era of COVID-19 pandemic.

Development and validation of a risk-adjustment model for mortality and hospital length of stay for trauma patients: a prospective registry-based study in Australia

ObjectivesAdequate risk adjustment for factors beyond the control of the healthcare system contributes to the process of transparent and equitable benchmarking of trauma outcomes. Current risk adjustment models are not...

A scoring system derived from electronic health records to identify patients at high risk for noninvasive ventilation failure

ObjectiveTo develop and validate a clinical risk prediction score for noninvasive ventilation (NIV) failure defined as intubation after a trial of NIV in non-surgical patients.DesignRetrospective cohort study of a multihospital electronic health record database.PatientsNon-surgical adult patients receiving NIV as the first method of ventilation within two days of hospitalization.MeasurementPrimary outcome was intubation after a trial of NIV. We used a non-random split of the cohort based on year of admission for model development and validation. We included subjects admitted in years 2010–2014 to develop a risk prediction model and built a parsimonious risk scoring model using multivariable logistic regression. We validated the model in the cohort of subjects hospitalized in 2015 and 2016.Main resultsOf all the 47,749 patients started on NIV, 11.7% were intubated. Compared with NIV success, those who were intubated had worse mortality (25.2% vs. 8.9%). Strongest independent predictors for intubation were organ failure, principal diagnosis group (substance abuse/psychosis, neurological conditions, pneumonia, and sepsis), use of invasive ventilation in the prior year, low body mass index, and tachypnea. The c-statistic was 0.81, 0.80 and 0.81 respectively, in the derivation, validation and full cohorts. We constructed three risk categories of the scoring system built on the full cohort; the median and interquartile range of risk of intubation was: 2.3% [1.9%–2.8%] for low risk group; 9.3% [6.3%–13.5%] for intermediate risk category; and 35.7% [31.0%–45.8%] for high risk category.ConclusionsIn patients started on NIV, we found that in addition to factors known to be associated with intubation, neurological, substance abuse, or psychiatric diagnoses were highly predictive for intubation. The prognostic score that we have developed may provide quantitative guidance for decision-making in patients who are started on NIV.

Parsimonious Credit Risk Pricing Model for Corporate Bonds and Credit Derivatives Markets

Parsimonious Credit Risk Pricing Model for Corporate Bonds and Credit Derivatives Markets

Developing and validating subjective and objective risk-assessment measures for predicting mortality after major surgery: An international prospective cohort study

Preoperative risk prediction is important for guiding clinical decision-making and resource allocation. Clinicians frequently rely solely on their own clinical judgement for risk prediction rather than objective measures. We aimed to compare the accuracy of freely available objective surgical risk tools with subjective clinical assessment in predicting 30-day mortality. We conducted a prospective observational study in 274 hospitals in the United Kingdom (UK), Australia, and New Zealand. For 1 week in 2017, prospective risk, surgical, and outcome data were collected on all adults aged 18 years and over undergoing surgery requiring at least a 1-night stay in hospital. Recruitment bias was avoided through an ethical waiver to patient consent; a mixture of rural, urban, district, and university hospitals participated. We compared subjective assessment with 3 previously published, open-access objective risk tools for predicting 30-day mortality: the Portsmouth-Physiology and Operative Severity Score for the enUmeration of Mortality (P-POSSUM), Surgical Risk Scale (SRS), and Surgical Outcome Risk Tool (SORT). We then developed a logistic regression model combining subjective assessment and the best objective tool and compared its performance to each constituent method alone. We included 22,631 patients in the study: 52.8% were female, median age was 62 years (interquartile range [IQR] 46 to 73 years), median postoperative length of stay was 3 days (IQR 1 to 6), and inpatient 30-day mortality was 1.4%. Clinicians used subjective assessment alone in 88.7% of cases. All methods overpredicted risk, but visual inspection of plots showed the SORT to have the best calibration. The SORT demonstrated the best discrimination of the objective tools (SORT Area Under Receiver Operating Characteristic curve [AUROC] = 0.90, 95% confidence interval [CI]: 0.88-0.92; P-POSSUM = 0.89, 95% CI 0.88-0.91; SRS = 0.85, 95% CI 0.82-0.87). Subjective assessment demonstrated good discrimination (AUROC = 0.89, 95% CI: 0.86-0.91) that was not different from the SORT (p = 0.309). Combining subjective assessment and the SORT improved discrimination (bootstrap optimism-corrected AUROC = 0.92, 95% CI: 0.90-0.94) and demonstrated continuous Net Reclassification Improvement (NRI = 0.13, 95% CI: 0.06-0.20, p < 0.001) compared with subjective assessment alone. Decision-curve analysis (DCA) confirmed the superiority of the SORT over other previously published models, and the SORT-clinical judgement model again performed best overall. Our study is limited by the low mortality rate, by the lack of blinding in the 'subjective' risk assessments, and because we only compared the performance of clinical risk scores as opposed to other prediction tools such as exercise testing or frailty assessment. In this study, we observed that the combination of subjective assessment with a parsimonious risk model improved perioperative risk estimation. This may be of value in helping clinicians allocate finite resources such as critical care and to support patient involvement in clinical decision-making.

A Parsimonious Risk Model to Assist Managers in Deciding When to Close and Reopen Business Premises Based on the Probability of Viral Infection

The human, business and economic impact of the COVID-19 pandemic has been unprecedented Here it is argued that basic probability theory, combined with simple scenario planning, can be of value in the management of business risk alongside human resources planning Specifically, we develop guidance on when to close and later reopen group-work office spaces based on the group size and estimated probability that one or more persons within a given group may have contracted the virus Examples are included from the live scenario planning exercise conducted by the authors in managing their own businesses

Robo-Advisors: Machine Learning in Trend-Following ETF Investments

We examine an application of machine learning to exchange traded fund investments in the U.S. market. To find how the changes in exchange traded fund prices are associated with expected market fundamentals, we propose three parsimonious risk factors extracted from various U.S. economic and market indicators. Based on the information set including these three factors, we build a predictive support vector machine model that can detect long or short investment signals. We find that the high probability of an upward momentum from our forecasting model suggests a long exchange traded fund signal, whereas the low probability of a downward momentum indicates a short exchange traded fund signal. We further design an algorithmic trading system with the support vector machine factor model. We find that the trading system shows practically desirable and robust performances over in-sample and out-of-sample trading periods

Maternal Risk Modeling in Critical Care-Development of a Multivariable Risk Prediction Model for Death and Prolonged Intensive Care.

We aimed to develop and validate an accurate risk prediction model for both mortality and a combined outcome of mortality and morbidity for maternal admissions to critical care. We used data from a high-quality prospectively collected national database, supported with literature review and expert opinion. We tested univariable associations between each risk factor and outcome. We then developed two separate multivariable logistic regression models for the outcomes of acute hospital mortality and death or prolonged ICU length of stay. We validated two parsimonious risk prediction models specific for a maternal population. The Intensive Care National Audit and Research Centre Case Mix Programme is the national clinical audit for adult critical care in England, Wales, and Northern Ireland. All female admissions to adult general critical care units, for the period January 1, 2007-December 31, 2016, 16-50 years old, and admitted either while pregnant or within 42 days of delivery-a cohort of 15,480 women. None. We aimed to develop and validate an accurate risk prediction model for both mortality and a combined outcome of mortality and morbidity for maternal admissions to critical care. For the primary outcome of acute hospital mortality, our parsimonious risk model consisting of eight variables had an area under the receiver operating characteristic of 0.96 (95% CI, 0.91-1.00); these variables are commonly available for all maternal admissions. For the secondary composite outcome of death or ICU length of stay greater than 48 hours, the risk model consisting of 17 variables had an area under the receiver operating characteristic of 0.80 (95% CI, 0.78-0.83). We developed risk prediction models specific to the maternal critical care population. The models compare favorably against general adult ICU risk prediction models in current use within this population.

A Parsimonious Risk Model to Assist Managers in Deciding When to Close and Reopen Business Premises Based on the Probability of Viral Infection

The human, business and economic impact of the COVID-19 pandemic has been unprecedented. Here it is argued that basic probability theory, combined with simple scenario planning, can be of value in the management of business risk alongside human resources planning. Specifically, we develop guidance on when to close and later reopen group-work office spaces based on the group size and estimated probability that one or more persons within a given group may have contracted the virus. Examples are included from the live scenario planning exercise conducted by the authors in managing their own businesses.