Clinical prediction model for pulmonary thrombosis diagnosis in hospitalized patients with SARS-CoV-2 infection.

Background and Aim: We aimed to develop a clinical prediction model for pulmonary thrombosis (PT) diagnosis in hospitalized COVID-19 patients. Methods: Non-intensive care unit hospitalized COVID-19 patients who underwent a computed tomography pulmonary angiogram (CTPA) for suspected PT were included in the study. Demographic, clinical, analytical, and radiological variables as potential factors associated with the presence of PT were selected. Multivariable Cox regression analysis to develop a score for estimating the pre-test probability of PT was performed. The score was internally validated by bootstrap analysis. Results: Among the 271 patients who underwent a CTPA, 132 patients (48.7%) had PT. Heart rate >100 bpm (OR = 4.63 [95% CI: 2.30-9.34]; P < 0.001), respiratory rate >22 bpm (OR = 5.21 [95% CI: 2.00-13.54]; P < 0.001), RALE score ≥4 (OR = 3.24 [95% CI: 1.66-6.32]; P < 0.001), C-reactive protein (CRP) >100 mg/L (OR = 2.10 [95% CI: 0.95-4.63]; P = 0.067), and D-dimer >3.000 ng/mL (OR = 6.86 [95% CI: 3.54-13.28]; P < 0.001) at the time of suspected PT were independent predictors of thrombosis. Using these variables, we constructed a nomogram (CRP, Heart rate, D-dimer, RALE score, and respiratory rate [CHEDDAR score]) for estimating the pre-test probability of PT. The score showed a high predictive accuracy (area under the receiver-operating characteristics curve = 0.877; 95% CI: 0.83-0.92). A score lower than 182 points on the nomogram confers a low probability for PT with a negative predictive value of 92%. Conclusions: CHEDDAR score can be used to estimate the pre-test probability of PT in hospitalized COVID-19 patients outside the intensive care unit. Relevance for Patients: Developing a new clinical prediction model for PT diagnosis in COVID-19 may help in the triage of patients, and limit unnecessary exposure to radiation and the risk of nephrotoxicity due to iodinated contrast.

Relationship Between Medical Questionnaire and Influenza Rapid Test Positivity: Subjective Pretest Probability, "I Think I Have Influenza," Contributes to the Positivity Rate.

Introduction Rapid influenza diagnostic tests (RIDTs) are considered essential for determining when to start influenza treatment using anti-influenza drugs, but their accuracy is about 70%. Under the COVID-19 pandemic, we hope to refrain from performing unnecessary RIDTs considering droplet infection of COVID-19 and influenza. We re-examined the medical questionnaire's importance and its relationship to the positivity of RIDTs. Then we built a positivity prediction model for RIDTs using automated artificial intelligence (AI). Methods We retrospectively investigated 96 patients who underwent RIDTs at the outpatient department from December 2019 to March 2020. We used a questionnaire sheet with 24 items before conducting RIDTs. The factors associated with the positivity of RIDTs were statistically analyzed. We then used an automated AI framework to produce the positivity prediction model using the 24 items, sex, and age, with five-fold cross-validation. Results Of the 47 women and 49 men (median age was 39 years), 56 patients were RIDT positive with influenza A. The AI-based model using 26 variables had an area under the curve (AUC) of 0.980. The stronger variables are subjective pretest probability, which is a numerically described score ranging from 0% to 100% of "I think I have influenza," cough, past hours after the onset, muscle pain, and maximum body temperature in order. Conclusion We easily built the RIDT positivity prediction model using automated AI. Its AUC was satisfactory, and it suggested the importance of a detailed medical interview. Both the univariate analysis and AI-based model suggested that subjective pretest probability, "I think I have influenza," might be useful.

COVID-19 Preprocedural Testing: What About the False Positives?

In the COVID-19 era, preprocedural patients are almost uniformly screened for symptoms, asked to quarantine preoperatively, and then undergo a test of uncertain validity with very low pretest probability. A small percentage of these tests return positive. As a result, surgical procedures are delayed and patients are required to quarantine. Are these asymptomatic patients truly positive for COVID-19? What are the impacts of these test results on the patient and the health care system? In the following commentary, we review how the uncertain validity of reverse transcription polymerase chain reaction testing combined with a low-prevalence population predisposes for false-positive results. As a mitigation strategy, we ask that readers refocus on the fundamental principal of diagnostic testing: pretest probability.

Improving CT accuracy in the diagnosis of COVID-19 in a hospital setting.

OBJECTIVE: This study aimed to improve the accuracy of CT for detection of COVID-19-associated pneumonia and to identify patient subgroups who might benefit most from CT imaging. METHODS: A total of 269 patients who underwent CT for suspected COVID-19 were included in this retrospective analysis. COVID-19 was confirmed by reverse-transcription-polymerase-chain-reaction. Basic demographics (age and sex) and initial vital parameters (O2-saturation, respiratory rate, and body temperature) were recorded. Generalized mixed models were used to calculate the accuracy of vital parameters for detection of COVID-19 and to evaluate the diagnostic accuracy of CT. A clinical score based on vital parameters, age, and sex was established to estimate the pretest probability of COVID-19 and used to define low, intermediate, and high risk groups. A p-value of <0.05 was considered statistically significant. RESULTS: The sole use of vital parameters for the prediction of COVID-19 was inferior to CT. After correction for confounders, such as age and sex, CT showed a sensitivity of 0.86, specificity of 0.78, and positive predictive value of 0.36. In the subgroup analysis based on pretest probability, positive predictive value and sensitivity increased to 0.53 and 0.89 in the high-risk group, while specificity was reduced to 0.68. In the low-risk group, sensitivity and positive predictive value decreased to 0.76 and 0.33 with a specificity of 0.83. The negative predictive value remained high (0.94 and 0.97) in both groups. CONCLUSIONS: The accuracy of CT for the detection of COVID-19 might be increased by selecting patients with a high-pretest probability of COVID-19.

The theoretical mortality risk of an asymptomatic patient with a negative SARS-CoV-2 test developing COVID-19 following elective orthopaedic surgery.

AIMS: The risk to patients and healthcare workers of resuming elective orthopaedic surgery following the peak of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has been difficult to quantify. This has prompted governing bodies to adopt a cautious approach that may be impractical and financially unsustainable. The lack of evidence has made it impossible for surgeons to give patients an informed perspective of the consequences of elective surgery in the presence of SARS-CoV-2. This study aims to determine, for the UK population, the probability of a patient being admitted with an undetected SARS-CoV-2 infection and their resulting risk of death; taking into consideration the current disease prevalence, reverse transcription-polymerase chain reaction (RT-PCR) testing, and preassessment pathway. METHODS: The probability of SARS-CoV-2 infection with a false negative test was calculated using a lower-end RT-PCR sensitivity of 71%, specificity of 95%, and the UK disease prevalence of 0.24% reported in May 2020. Subsequently, a case fatality rate of 20.5% was applied as a worst-case scenario. RESULTS: The probability of SARS-CoV-2 infection with a false negative preoperative test was 0.07% (around 1 in 1,400). The risk of a patient with an undetected infection being admitted for surgery and subsequently dying from the coronavirus disease 2019 (COVID-19) is estimated at approximately 1 in 7,000. However, if an estimate of the current global infection fatality rate (1.04%) is applied, the risk of death would be around 1 in 140,000, at most. This calculation does not take into account the risk of nosocomial infection. Conversely, it does not factor in that patients will also be clinically assessed and asked to self-isolate prior to surgery. CONCLUSION: Our estimation suggests that the risk of patients being inadvertently admitted with an undetected SARS-CoV-2 infection for elective orthopaedic surgery is relatively low. Accordingly, the risk of death following elective orthopaedic surgery is low, even when applying the worst-case fatality rate. Cite this article: Bone Joint J 2020;102-B(9):1256-1260.