Validation of Chest Computed Tomography Artificial Intelligence to Determine the Requirement for Mechanical Ventilation and Risk of Mortality in Hospitalized Coronavirus Disease-19 Patients in a Tertiary Care Center In Mexico City.

BACKGROUND: Artificial intelligence (AI) in radiology has improved diagnostic performance and shortened reading times of coronavirus disease 2019 (COVID-19) patients' studies. OBJECTIVES: The objectives pf the study were to analyze the performance of a chest computed tomography (CT) AI quantitative algorithm for determining the risk of mortality/mechanical ventilation (MV) in hospitalized COVID-19 patients and explore a prognostic multivariate model in a tertiary-care center in Mexico City. METHODS: Chest CT images of 166 COVID-19 patients hospitalized from April 1 to 20, 2020, were retrospectively analyzed using AI algorithm software. Data were collected from their medical records. We analyzed the diagnostic yield of the relevant CT variables using the area under the ROC curve (area under the curve [AUC]). Optimal thresholds were obtained using the Youden index. We proposed a predictive logistic model for each outcome based on CT AI measures and predetermined laboratory and clinical characteristics. RESULTS: The highest diagnostic yield of the assessed CT variables for mortality was the percentage of total opacity (threshold >51%; AUC = 0.88, sensitivity = 74%, and specificity = 91%). The AUC of the CT severity score (threshold > 12.5) was 0.88 for MV (sensitivity = 65% and specificity = 92%). The proposed prognostic models include the percentage of opacity and lactate dehydrogenase level for mortality and troponin I and CT severity score for MV requirement. CONCLUSION: The AI-calculated CT severity score and total opacity percentage showed good diagnostic accuracy for mortality and met MV criteria. The proposed prognostic models using biochemical variables and imaging data measured by AI on chest CT showed good risk classification in our population of hospitalized COVID-19 patients.

Epidemiology of Mycobacterium abscessus.

BACKGROUND: Nontuberculous mycobacteria (NTM) are highly abundant in soil, dust, and water sources, making human-pathogen contact frequent and recurrent. NTM represents over 200 species/subspecies; some are considered strict or opportunistic pathogens. Mycobacterium abscessus, often regarded as one of the most antibiotic-resistant mycobacteria, is the second most frequent NTM pulmonary disease pathogen. OBJECTIVES: To describe the epidemiology of M. abscessus through a literature review focusing on clinical aspects. SOURCES: We conducted searches on PubMed and Web of Knowledge for articles published from 2010 to the present using the keywords 'Mycobacterium abscessus', 'Nontuberculous mycobacteria', and 'epidemiology'. Our search prioritized original reports on the occurrence of NTM and M. abscessus infection/disease. CONTENT: Advanced molecular and genetic diagnostic techniques have refined the M. abscessus complex (MABC) microbiological classification over the last few decades. MABC can adhere to surfaces and form a biofilm. This characteristic and its resistance to common disinfectants allow these microorganisms to persist in the water distribution systems, becoming a constant reservoir. The frequency and manifestation of NTM species vary geographically because of environmental conditions and population susceptibility differences. MABC lung disease, the most frequent site of NTM infection in humans, is often seen in patients with underlying lung diseases such as bronchiectasis, whereas MABC disseminated disease is related to immunosuppression. Skin and soft tissue infections are associated with surgical or injection procedures. Epidemiological evidence suggests an overall increase in MABC infection and disease in the last decade. IMPLICATIONS: Establishing the burden of this disease is challenging because of varying measures of incidence and prevalence, referral bias, and differences in medical practices and reporting. Furthermore, environmental and structural determinants, infection routes, and MABC pulmonary disease mechanisms require additional investigation. This review contributes to a better understanding of the epidemiology of MABC, which could inform clinical practice and future research.

Prognostic utility of the chest computed tomography severity score for the requirement of mechanical ventilation and mortality in hospitalized patients with COVID-19.

Purpose: To correlate the chest computed tomography severity score (CT-SS) with the need for mechanical ventilation and mortality in hospitalized patients with COVID-19. Materials and methods: The chest CT images of 224 inpatients with COVID-19, confirmed by reverse transcriptase-polymerase chain reaction (RT-PCR), were retrospectively reviewed from April 1 to 25, 2020, in a tertiary health care center. We calculated the CT-SS (dividing each lung into 20 segments and assigning scores of 0, 1, and 2 due to opacification involving 0%, <50%, and ≥50% of each region for a global range of 0-40 points, including both lungs), and collected clinical data. The receiver operating characteristic curve and Youden Index analysis was performed to calculate the CT-SS threshold and accuracy for classification for risk of mortality or MV requirement. Results: 136 men and 88 women were recruited, with an age range of 23-91 years and a mean of 50.17 years; 79 met the MV criteria, and 53 were nonsurvivors. The optimal threshold was >27.5 points for mortality (area under ROC curve >0.96), with a sensitivity of 93% and specificity of 87%, and >25.5 points for the need for MV (area under ROC curve >0.94), with a sensitivity of 90% and specificity of 89%. The Kaplan-Meier curves show a significant difference in mortality by the CT-SS threshold (Log Rank p < 0.001). Conclusions: In our cohort of hospitalized patients with COVID-19, the CT-SS accurately discriminates the need for MV and mortality risk. In conjunction with clinical status and laboratory data, the CT-SS may be a useful imaging tool that could be included in establishing a prognosis for this population.

Semiautomatic assessment of whole-body tumor burden with 18F-PSMA-1007 in biochemical recurrent prostate cancer.

OBJECTIVE: The aim of the study was to evaluate the 18F-PSMA-1007 PET/computed tomography (CT) semiautomatic volumetric parameters to assess the whole-body tumor burden and its correlation with prostate-specific antigen (PSA) and Gleason score in patients with biochemically recurrent prostate cancer (PCa). MATERIALS AND METHODS: A total of 110 patients referred for 18F-PSMA-1007 PET/CT due to biochemical recurrence were retrospectively analyzed. Whole-body total lesion prostate-specific membrane antigen (wbTl-PSMA) and whole-body PSMA-derived tumor volume (wbPSMA-TV) metrics on 18F-PSMA-1007 were obtained semiautomatically in dedicated software. A Spearman test was performed to explore the correlation of volumetric imaging parameters with PSA levels and Gleason score. To analyze the association between volumetric measures and PSA subgroups, we used a Kruskal-Wallis test and a Dunn's test to identify each group causing an observed difference. RESULTS: A total of 492 metastatic lesions were analyzed, and a significant correlation was found between wbTL-PSMA (R = 0.63, P < 0.0001) and wbPSMA-TV (R = 0.49, P < 0.0001) with serum PSA. A statistically significant difference with wbTL-PSMA was found in patients with a PSA less than or equal 0.5 ng/ml and PSA in the range of 0.51-1.0 ng/ml. CONCLUSION: 18F-PSMA-1007 PSMA volumetric parameters can provide a quantitative imaging biomarker for whole-body tumor burden.

[Evaluation of the characteristics of aortic dissection by computed angio-tomography in the Mexican population]. / Evaluación de las características de la disección aórtica en la población mexicana mediante angiotomografía computarizada.

OBJECTIVE: To review aortic dissection (AD) in the Mexican population. METHOD: A retrospective study was conducted using 434 medical records of patients with aortic angio-tomography between November 2014 and October 2015. A sample was obtained of 32 patients with a first time diagnosis of AD. An analysis was performed of the dissections according to gender, age group, Stanford/De Bakey classification, and mortality rate 6 months after diagnosis. Statistical analysis was performed by obtaining the Chi squared index for the independent variables of gender, Marfan syndrome, systemic arterial hypertension, as well as calcified atheromatous disease in association with dissection subtypes, re-entry sites, and hypo-perfusion signs. RESULTS: The patients included 65.6% males with a mean age of 54.5 years, and 34.4% females with mean age of 42.5 years. The most common dissection subtype was B/3. Mortality rate at 6 months was 18.7%. There was a significant association, with a marginal P in patients with Marfan syndrome and Stanford subtypes of AD (P=.0506). There was a significant association in patients with abdominal aortic aneurysm, when compared with Stanford subtypes of AD (P=.047104). CONCLUSIONS: AD is an emergency in which diagnosis and timely management are essential to improve prognosis. In the sample presented here, a significant association was found in patients with a history of Marfan syndrome and abdominal aneurysms with dissections according to the Stanford classification. The rest of the independent variables did not show any significant association, probably related to the size of the sample.

[Diagnostic accuracy of the automatic quantification software in patients with suspected COVID-19 of the Instituto Nacional de Cardiology Ignacio Chávez]. / Precisión diagnóstica del software de cuantificación automática en pacientes con sospecha de COVID-19 del Instituto Nacional de Cardiología Ignacio Chávez.

Objetivos: Establecer la precisión diagnóstica por tomografía computarizada (TC) de la probabilidad de neumopatía por enfermedad por coronavirus 2019 (COVID-19), dada por el sistema de inteligencia artificial (IA) diseñado por Siemens, y el resultado de la evaluación cualitativa CO-RADS (COVID-19 Reporting and Data System) con el estándar de referencia reacción en cadena de la polimerasa transcriptasa inversa (RT-PCR), entregando así la experiencia de nuestra institución. Métodos: Se realizó un estudio observacional, comparativo y retrolectivo en 192 pacientes adultos con sospecha de infección por coronavirus 2 del síndrome respiratorio agudo grave (SARS-CoV-2) que contaban con prueba PCR. Se obtuvo la información de precisión diagnóstica luego de comparar el estándar de referencia (RT- PCR) con el CO-RADS realizado por los observadores y la probabilidad de COVID-19 que arrojaron las imágenes de TC mediante la IA. Resultados: La comparación de la probabilidad de COVID-19 obtenida por la IA vs. la RT-PCR para SARS-CoV- 2 generó un AUC ROC de 0.774 (IC: 0.69-0.81) con p = 0.0001. La probabilidad de COVID-19 tuvo una precisión aceptable, con un buen valor predictivo positivo del 87.80%, pero con un pobre valor predictivo negativo del 58.80%. La variable CO-RADS vs. PCR obtuvo una mayor precisión con valores de sensibilidad y especificidad del 91.80 y 88.7% respectivamente. Conclusión: La comparación entre los resultados obtenidos por la IA y por la variable CO-RADS mostró mayor efectividad en esta última, sin embargo se logró documentar el alto impacto que tiene el sistema de cuantificación automática en la evaluación de estos pacientes, ya que permite agilizar la valoración del radiólogo y funciona como complemento en casos de dudas diagnósticas.

Validation of Chest Computed Tomography Artificial Intelligence to Determine the Requirement for Mechanical Ventilation and Risk of Mortality in Hospitalized Coronavirus Disease-19 Patients in a Tertiary Care Center In Mexico City

ABSTRACT Background: Artificial intelligence (AI) in radiology has improved diagnostic performance and shortened reading times of coronavirus disease 2019 (COVID-19) patients’ studies. Objectives: The objectives pf the study were to analyze the performance of a chest computed tomography (CT) AI quantitative algorithm for determining the risk of mortality/mechanical ventilation (MV) in hospitalized COVID-19 patients and explore a prognostic multivariate model in a tertiary-care center in Mexico City. Methods: Chest CT images of 166 COVID-19 patients hospitalized from April 1 to 20, 2020, were retrospectively analyzed using AI algorithm software. Data were collected from their medical records. We analyzed the diagnostic yield of the relevant CT variables using the area under the ROC curve (area under the curve [AUC]). Optimal thresholds were obtained using the Youden index. We proposed a predictive logistic model for each outcome based on CT AI measures and predetermined laboratory and clinical characteristics. Results: The highest diagnostic yield of the assessed CT variables for mortality was the percentage of total opacity (threshold >51%; AUC = 0.88, sensitivity = 74%, and specificity = 91%). The AUC of the CT severity score (threshold > 12.5) was 0.88 for MV (sensitivity = 65% and specificity = 92%). The proposed prognostic models include the percentage of opacity and lactate dehydrogenase level for mortality and troponin I and CT severity score for MV requirement. Conclusion: The AI-calculated CT severity score and total opacity percentage showed good diagnostic accuracy for mortality and met MV criteria. The proposed prognostic models using biochemical variables and imaging data measured by AI on chest CT showed good risk classification in our population of hospitalized COVID-19 patients.

Evaluación de las características de la disección aórtica en la población mexicana mediante angiotomografía computarizada / Evaluation of the characteristics of aortic dissection by computed angio-tomography in the Mexican population

Resumen Objetivo: Revisión y análisis de la disección aórtica (DA) en la población mexicana. Método: Revisión retrospectiva de 434 expedientes electrónicos de pacientes con angiotomografía de aorta entre noviembre de 2014 y octubre de 2015. Se obtuvo una muestra de 32 pacientes con diagnóstico de DA de primera vez. Se realizó un análisis de las DA según género, grupo etario, clasificación de Stanford/De Bakey y mortalidad a 6 meses del diagnóstico. Se realizó análisis de significación estadística mediante la Chi-cuadrada para las variables independientes de género, síndrome de Marfan, hipertensión arterial sistémica y enfermedad ateromatosa calcificada en asociación con subtipos, sitios de reentrada y datos de hipoperfusión. Resultados: El 65.6% de los pacientes fueron masculinos, con un promedio de edad de 54.5 años, y el 34.4% fueron femeninos, con un promedio de edad de 42.5 años. El subtipo B/3 fue el más frecuentemente diagnosticado. La tasa de mortalidad a 6 meses fue del 18.7%. Se halló asociación significativa con p marginal en pacientes con síndrome de Marfan y subtipos de DA según Stanford (p = 0.0506), así como asociación significativa en pacientes con aneurisma de aorta abdominal y subtipos de DA según Stanford (p = 0.047104). Conclusiones: La DA es una emergencia en la cual el diagnóstico por imagen y el manejo oportuno son fundamentales para mejorar el pronóstico. En nuestra muestra encontramos asociación significativa de pacientes con antecedente de síndrome de Marfan y aneurisma aórtico abdominal con disecciones según la categoría de Stanford. El resto de las variables independientes no mostraron asociación significativa, en probable relación con el tamaño de la muestra.

Abstract Objective: To review aortic dissection (AD) in the Mexican population. Method: A retrospective study was conducted using 434 medical records of patients with aortic angio-tomography between November 2014 and October 2015. A sample was obtained of 32 patients with a first time diagnosis of AD. An analysis was performed of the dissections according to gender, age group, Stanford/De Bakey classification, and mortality rate 6 months after diagnosis. Statistical analysis was performed by obtaining the Chi squared index for the independent variables of gender, Marfan syndrome, systemic arterial hypertension, as well as calcified atheromatous disease in association with dissection subtypes, re-entry sites, and hypo-perfusion signs. Results: The patients included 65.6% males with a mean age of 54.5 years, and 34.4% females with mean age of 42.5 years. The most common dissection subtype was B/3. Mortality rate at 6 months was 18.7%. There was a significant association, with a marginal P in patients with Marfan syndrome and Stanford subtypes of AD (P = .0506). There was a significant association in patients with abdominal aortic aneurysm, when compared with Stanford subtypes of AD (P = .047104). Conclusions: AD is an emergency in which diagnosis and timely management are essential to improve prognosis. In the sample presented here, a significant association was found in patients with a history of Marfan syndrome and abdominal aneurysms with dissections according to the Stanford classification. The rest of the independent variables did not show any significant association, probably related to the size of the sample.