Utility of a Clinical Scoring System for Point of Care Triaging in COVID-19 Pneumonia

BackgroundSurges in COVID-19 disease cases can rapidly overwhelm healthcare resources; triaging to appropriate levels of care can assist in resource planning. At the beginning of the pandemic, we developed a simple triage tool, the Temple COVID-19 Pneumonia Triage Tool (TemCOV) based on a combination of clinical and radiographic features that are readily available on presentation to categorize and predict illness severity. MethodsWe prospectively examined 579 sequential cases admitted to Temple University Hospital who were assigned severity categories on admission. Our primary outcome was to compare the performance of TemCOV in predicting patients who have the highest likely of admission to the ICU at 24 and at 72 hours to other standard triage tools: the National Early Warning System (NEWS), the Modified Early Warning System (MEWS) and the CURB65 score. Additional endpoints included need for invasive mechanical ventilation (IMV) within 72 hours, total hospital admission charges, and mortality. Results26% of patients fell within our highest risk Category 4 and were more likely to require ICU admission at 24 hours (OR 11.51) and 72 hours (OR 8.6). Additionally they had the highest likelihood of needing IMV (OR 29.47) and in-hospital mortality (OR 2.37)., TemCOV performed similar to MEWS in predicting ICU admission at 24 hours (receive operator characteristic (ROC) curve area under the curve (AUC) 0.77 vs. 0.74, p=0.21) but better than NEWS2 and CURB65 (ROC AUC 0.77 vs. 0.69 and 0.77 vs. 0.64, respectively, p<0.01). While all severity scores had a weak correlation to hospital charges, the TemCOV performed the best among all severity scores measured (r=0.18); median hospital charges for Category 4 patients was $170,468 ($96,972-$487,556). ConclusionTemCOV is a simple triage score that can be used upon hospitalization in patients with COVID-19 that predicts the need for hospital resources such as ICU bed capacity, invasive mechanical ventilation and personnel staffing.

A Data Driven Change-point Epidemic Model for Assessing the Impact of Large Gathering and Subsequent Movement Control Order on COVID-19 Spread in Malaysia

The second wave of COVID-19 in Malaysia is largely attributed to a mass gathering held in Sri Petaling between February 27, 2020 and March 1, 2020, which contributed to an exponential rise of COVID-19 cases in the country. Starting March 18, 2020, the Malaysian government introduced four consecutive phases of a Movement Control Order (MCO) to stem the spread of COVID-19. The MCO was implemented through various non-pharmaceutical interventions (NPIs). The reported number of cases reached its peak by the first week of April and then started to reduce, hence proving the effectiveness of the MCO. To gain a quantitative understanding of the effect of MCO on the dynamics of COVID-19, this paper develops a class of mathematical models to capture the disease spread before and after MCO implementation in Malaysia. A heterogeneous variant of the Susceptible-Exposed-Infected-Recovered (SEIR) model is developed with additional compartments for asymptomatic transmission. Further, a change-point is incorporated to model the before and after disease dynamics, and is inferred based on data. Related statistical analyses for inference are developed in a Bayesian framework and are able to provide quantitative assessments of (1) the impact of the Sri Petaling gathering, and (2) the extent of decreasing transmission during the MCO period. The analysis here also quantitatively demonstrates how quickly transmission rates fall under effective NPI implemention within a short time period.

Forecasting daily confirmed COVID-19 cases in Malaysia using ARIMA models.

INTRODUCTION: The novel coronavirus infection has become a global threat affecting almost every country in the world. As a result, it has become important to understand the disease trends in order to mitigate its effects. The aim of this study is firstly to develop a prediction model for daily confirmed COVID-19 cases based on several covariates, and secondly, to select the best prediction model based on a subset of these covariates. METHODOLOGY: This study was conducted using daily confirmed cases of COVID-19 collected from the official Ministry of Health, Malaysia (MOH) and John Hopkins University websites. An Autoregressive Integrated Moving Average (ARIMA) model was fitted to the training data of observed cases from 22 January to 31 March 2020, and subsequently validated using data on cases from 1 April to 17 April 2020. The ARIMA model satisfactorily forecasted the daily confirmed COVID-19 cases from 18 April 2020 to 1 May 2020 (the testing phase). RESULTS: The ARIMA (0,1,0) model produced the best fit to the observed data with a Mean Absolute Percentage Error (MAPE) value of 16.01 and a Bayes Information Criteria (BIC) value of 4.170. The forecasted values showed a downward trend of COVID-19 cases until 1 May 2020. Observed cases during the forecast period were accurately predicted and were placed within the prediction intervals generated by the fitted model. CONCLUSIONS: This study finds that ARIMA models with optimally selected covariates are useful tools for monitoring and predicting trends of COVID-19 cases in Malaysia.

High Resolution CHEST CT(HRCT) Evaluation in Patients Hospitalized with COVID-19 Infection

IntroductionCurrently the main diagnostic modality for COVID-19 (Coronavirus disease-2019) is reverse transcriptase polymerase chain reaction (RT-PCR) via nasopharyngeal swab which has high false negative rates. We evaluated the performance of high-resolution computed tomography (HRCT) imaging in the diagnosis of suspected COVID-19 infection compared to RT-PCR nasopharyngeal swab alone in patients hospitalized for suspected COVID-19 infection. MethodsThis was a retrospective analysis of 324 consecutive patients admitted to Temple University Hospital. All hospitalized patients who had RT-PCR testing and HRCT were included in the study. HRCTs were classified as Category 1, 2 or 3. Patients were then divided into four groups based on HRCT category and RT-PCR swab results for analysis. ResultsThe average age of patients was 59.4 ({+/-}15.2) years and 123 (38.9%) were female. Predominant ethnicity was African American 148 (46.11%). 161 patients tested positive by RT-PCR, while 41 tested positive by HRCT. 167 (52.02%) had category 1 scan, 63 (19.63%) had category 2 scan and 91 (28.35%) had category 3 HRCT scans. There was substantial agreement between our radiologists for HRCT classification ({kappa} = 0.64). Sensitivity and specificity of HRCT classification system was 77.6 and 73.7 respectively. Ferritin, LDH, AST and ALT were higher in Group 1 and D-dimers levels was higher in Group 3; differences however were not statistically significant. ConclusionDue to its high infectivity and asymptomatic transmission, until a highly sensitive and specific COVID-19 test is developed, HRCT should be incorporated into the assessment of patients who are hospitalized with suspected COVID-19. Key PointsO_ST_ABSKey QuestionC_ST_ABSCan High Resolution CT chest (HRCT) improve diagnostic accuracy of current Nasopharyngeal swab in suspected COVID-19 patients? Bottom LineIn this retrospective analysis, our novel HRCT classification identified 20% of all COVID-19 patients who had negative nasopharyngeal reverse transcriptase polymerase chain reaction (RT-PCR) tests but had HRCT findings consistent with COVID-19 pneumonia. These patients were ruled out for other infections and laboratory markers were similar to other RT-PCR positive patients Why Read onOur new HRCT classification when combined with RT-PCR can improve diagnostic accuracy while promptly improving triaging in COVID-19 patients.

CT colonography: effect of computer-aided detection of colonic polyps as a second and concurrent reader for general radiologists with moderate experience in CT colonography.

OBJECTIVES: To assess the effectiveness of computer-aided detection (CAD) as a second reader or concurrent reader in helping radiologists who are moderately experienced in computed tomographic colonography (CTC) to detect colorectal polyps. METHODS: Seventy CTC datasets (34 patients: 66 polyps ≥6 mm; 36 patients: no abnormalities) were retrospectively reviewed by seven radiologists with moderate CTC experience. After primary unassisted evaluation, a CAD second read and, after a time interval of ≥4 weeks, a CAD concurrent read were performed. Areas under the receiver operating characteristic (ROC) curve (AUC), along with per-segment, per-polyp and per-patient sensitivities, and also reading times, were calculated for each reader with and without CAD. RESULTS: Of seven readers, 86% and 71% achieved a higher accuracy (segment-level AUC) when using CAD as second and concurrent reader respectively. Average segment-level AUCs with second and concurrent CAD (0.853 and 0.864) were significantly greater (p < 0.0001) than average AUC in the unaided evaluation (0.781). Per-segment, per-polyp, and per-patient sensitivities for polyps ≥6 mm were significantly higher in both CAD reading paradigms compared with unaided evaluation. Second-read CAD reduced readers' average segment and patient specificity by 0.007 and 0.036 (p = 0.005 and 0.011), respectively. CONCLUSIONS: CAD significantly improves the sensitivities of radiologists moderately experienced in CTC for polyp detection, both as second reader and concurrent reader. KEY POINTS: • CAD helps radiologists with moderate CTC experience to detect polyps ≥6 mm. • Second and concurrent read CAD increase the radiologist's sensitivity for detecting polyps ≥6 mm. • Second read CAD slightly decreases specificity compared with an unassisted read. • Concurrent read CAD is significantly more time-efficient than second read CAD.