Assembling the Puzzle: Taking into Account Clinical Presentation and Predictive Scores.

Syncope is a common cause of emergency department visits. Physicians must scrutinize for life-threatening causes to avoid patient morbidity and mortality. Clinical decision rules are used to stratify risks and guide the course of action, including the need for further testing. This is the case of a 83-year-old man was brought to the emergency department after a 5-minute episode of sudden loss of consciousness. Vital signs showed hypotension and physical examination was unremarkable. Despite Wells score of 0, clinical suspicion for pulmonary embolism persisted, for which further testing was pursued. D-dimer was elevated at 13.77 mcg/mL and a chest computed tomography with angiography showed an extensive bilateral pulmonary embolism involving the distal right and left main pulmonary arteries. He was started on full-dose anticoagulation. This case exemplifies the need of high clinical suspicion along with the importance of applying predictive scores for diagnosing unusual causes of syncope.

A predictive score for COVID-19 diagnosis using clinical, laboratory and chest image data

Abstract Objectives Differential diagnosis of COVID-19 includes a broad range of conditions. Prioritizing containment efforts, protective personal equipment and testing can be challenging. Our aim was to develop a tool to identify patients with higher probability of COVID-19 diagnosis at admission. Methods This cross-sectional study analyzed data from 100 patients admitted with suspected COVID-19. Predictive models of COVID-19 diagnosis were performed based on radiology, clinical and laboratory findings; bootstrapping was performed in order to account for overfitting. Results A total of 29% of patients tested positive for SARS-CoV-2. Variables associated with COVID-19 diagnosis in multivariate analysis were leukocyte count ≤7.7 × 103 mm-3, LDH >273 U/L, and chest radiographic abnormality. A predictive score was built for COVID-19 diagnosis, with an area under ROC curve of 0.847 (95% CI 0.77-0.92), 96% sensitivity and 73.5% specificity. After bootstrapping, the corrected AUC for this model was 0.827 (95% CI 0.75-0.90). Conclusions Considering unavailability of RT-PCR at some centers, as well as its questionable early sensitivity, other tools might be used in order to identify patients who should be prioritized for testing, re-testing and admission to isolated wards. We propose a predictive score that can be easily applied in clinical practice. This score is yet to be validated in larger populations.

A predictive score for COVID-19 diagnosis using clinical, laboratory and chest image data.

OBJECTIVES: Differential diagnosis of COVID-19 includes a broad range of conditions. Prioritizing containment efforts, protective personal equipment and testing can be challenging. Our aim was to develop a tool to identify patients with higher probability of COVID-19 diagnosis at admission. METHODS: This cross-sectional study analyzed data from 100 patients admitted with suspected COVID-19. Predictive models of COVID-19 diagnosis were performed based on radiology, clinical and laboratory findings; bootstrapping was performed in order to account for overfitting. RESULTS: A total of 29% of patients tested positive for SARS-CoV-2. Variables associated with COVID-19 diagnosis in multivariate analysis were leukocyte count ≤7.7×103mm-3, LDH >273U/L, and chest radiographic abnormality. A predictive score was built for COVID-19 diagnosis, with an area under ROC curve of 0.847 (95% CI 0.77-0.92), 96% sensitivity and 73.5% specificity. After bootstrapping, the corrected AUC for this model was 0.827 (95% CI 0.75-0.90). CONCLUSIONS: Considering unavailability of RT-PCR at some centers, as well as its questionable early sensitivity, other tools might be used in order to identify patients who should be prioritized for testing, re-testing and admission to isolated wards. We propose a predictive score that can be easily applied in clinical practice. This score is yet to be validated in larger populations.

Prognostic factors of inhospital death in elderly patients: a time-to-event analysis of a cohort study in Martinique (French West Indies).

OBJECTIVE: The primary objective was to identify predictive factors of inhospital death in a population of patients aged 65 years or older hospitalised with Chikungunya virus (CHIKV) infection. The secondary aim was to develop and validate a predictive score for inhospital death based on the predictors identified. DESIGN: Longitudinal retrospective study from January to December 2014. SETTING: University Hospital of Martinique. PARTICIPANTS: Patients aged ≥65 years, admitted to any clinical ward and who underwent reverse transcription PCR testing for CHIKV infection. OUTCOME: Independent predictors of inhospital death were identified using multivariable Cox regression modelling. A predictive score was created using the adjusted HRs of factors associated with inhospital death. Receiver operating characteristic curve analysis was used to determine the best cut-off value. Bootstrap analysis was used to evaluate internal validity. RESULTS: Overall, 385 patients aged ≥65 years were included (average age: 80±8 years). Half were women, and 35 (9.1%) died during the hospital stay. Seven variables were found to be independently associated with inhospital death (concurrent cardiovascular disorders: HR 11.8, 95% CI 4.5 to 30.8; concurrent respiratory infection: HR 9.6, 95% CI 3.4 to 27.2; concurrent sensorimotor deficit: HR 7.6, 95% CI 2.0 to 28.5; absence of musculoskeletal pain: HR 2.6, 95% CI 1.3 to 5.3; history of alcoholism: HR 2.5, 95% CI 1.1 to 5.9; concurrent digestive symptoms: HR 2.4, 95% CI 1.2 to 4.9; presence of confusion or delirium: HR 2.1, 95% CI 1.1 to 4.2). The score ranged from 0 to 25, with an average of 6±6. The area under the curve was excellent (0.90; 95% CI 0.86 to 0.94). The best cut-off value was a score ≥8 points, with a sensitivity of 91% (82%-100%) and specificity of 75% (70%-80%). CONCLUSIONS: Signs observed by the clinician during the initial examination could predict inhospital death. The score will be helpful for early management of elderly subjects presenting within 7 days of symptom onset in the context of CHIKV outbreaks.

Post analysis simulated correlation of the El-Ganzouri airway difficulty score with difficult airway / Correlação simulada após análise dos escores de El-Ganzouri para via aérea difícil

ABSTRACT BACKGROUND: Difficult airway (DA) occurs frequently (5-15%) in clinical practice. The El-Ganzouri Risk Index (EGRI) has a high sensitivity for predicting a difficult intubation (DI). However difficult mask ventilation (DMV) was never included in the EGRI. Since DMV was not included in the EGRI assessment, and obstructive sleep apnea (OSA) is also correlated with DMV, a study correlating the prediction of DA and OSA (identified by STOP-Bang questionnaire, SB) seemed important. METHODS: We accessed a database previously collected for a post analysis simulation of the airway difficulty predictivity of the EGRI, associated with normal and difficult airway, particularly DMV. As secondary aim, we measured the correlation between the SB prediction system and DA, compared to the EGRI. RESULTS: A total of 2747 patients were included in the study. The proportion of patients with DI was 14.7% (95% CI 13.4-16) and the proportion of patients with DMV was 3.42% (95% CI 2.7-4.1). The incidence of DMV combined with DI was (2.3%). The optimal cutoff value of EGRI was 3. EGRI registered also an higher ability to predict DMV (AUC = 0.76 (95% CI 0.71-0.81)). Adding the SB variables in the logistic model, the AUC increases with the inclusion of "observed apnea" variable (0.83 vs. 0.81, p = 0.03). The area under the ROC curve for the patients with DI and DMV was 0.77 (95% CI 0.72-0.83). CONCLUSIONS: This study confirms that the incidence of DA is not negligible and suggests the use of the EGRI as simple bedside predictive score to improve patient safety.

RESUMO JUSTIFICATIVA: A via aérea difícil (VAD) ocorre com frequência (5-15%) na prática clínica. O Índice de Risco de El-Ganzouri (EGRI) tem uma alta sensibilidade para prever intubação difícil (ID). No entanto, a ventilação difícil via máscara (VDM) nunca foi incluída no EGRI. Como a VDM não foi incluída na avaliação EGRI e a apneia obstrutiva do sono (AOS) também está correlacionada com a VDM, um estudo que correlacionasse a previsão da VAD e AOS (identificada pelo questionário STOP-Bang, SB) pareceu importante. MÉTODOS: Acessamos um banco de dados previamente coletados para simular uma análise posterior da previsibilidade do EGRI para via aérea difícil, associado à via aérea normal e difícil, particularmente VDM. Como objetivo secundário, avaliamos a correlação entre o sistema de previsão do SB e da VAD, em comparação com o EGRI. RESULTADOS: Foram incluídos no estudo 2.747 pacientes. A proporção de pacientes com ID foi de 14,7% (IC de 95%; 13,4-16) e a proporção de pacientes com VDM foi de 3,42% (IC de 95% 2,7-4,1). A incidência da VDM combinada com a de ID foi de 2,3%. O valor de corte ideal do EGRI foi 3. EGRI também registrou uma capacidade maior de prever VDM (ASC = 0,76 (IC de 95%; 0,71-0,81)). Ao somar as variáveis do SB no modelo logístico, a ASC aumenta com a inclusão da variável "apneia observada" (0,83 vs. 0,81, p = 0,03). A área sob a curva ROC para os pacientes com ID e VDM foi de 0,77 (IC de 95%; 0,72-0,83). CONCLUSÕES: Este estudo confirma que a incidência de VAD não é desprezível e sugere o uso do EGRI como um escore de cabeceira preditivo simples para melhorar a segurança do paciente.

Post analysis simulated correlation of the El-Ganzouri airway difficulty score with difficult airway.

BACKGROUND: Difficult airway (DA) occurs frequently (5-15%) in clinical practice. The El-Ganzouri Risk Index (EGRI) has a high sensitivity for predicting a difficult intubation (DI). However difficult mask ventilation (DMV) was never included in the EGRI. Since DMV was not included in the EGRI assessment, and obstructive sleep apnea (OSA) is also correlated with DMV, a study correlating the prediction of DA and OSA (identified by STOP-Bang questionnaire, SB) seemed important. METHODS: We accessed a database previously collected for a post analysis simulation of the airway difficulty predictivity of the EGRI, associated with normal and difficult airway, particularly DMV. As secondary aim, we measured the correlation between the SB prediction system and DA, compared to the EGRI. RESULTS: A total of 2747 patients were included in the study. The proportion of patients with DI was 14.7% (95% CI 13.4-16) and the proportion of patients with DMV was 3.42% (95% CI 2.7-4.1). The incidence of DMV combined with DI was (2.3%). The optimal cutoff value of EGRI was 3. EGRI registered also an higher ability to predict DMV (AUC=0.76 (95% CI 0.71-0.81)). Adding the SB variables in the logistic model, the AUC increases with the inclusion of "observed apnea" variable (0.83 vs. 0.81, p=0.03). The area under the ROC curve for the patients with DI and DMV was 0.77 (95% CI 0.72-0.83). CONCLUSIONS: This study confirms that the incidence of DA is not negligible and suggests the use of the EGRI as simple bedside predictive score to improve patient safety.

[Post analysis simulated correlation of the El-Ganzouri airway difficulty score with difficult airway]. / Correlação simulada após análise dos escores de El-Ganzouri para via aérea difícil.

BACKGROUND: Difficult airway (DA) occurs frequently (5-15%) in clinical practice. The El-Ganzouri Risk Index (EGRI) has a high sensitivity for predicting a difficult intubation (DI). However difficult mask ventilation (DMV) was never included in the EGRI. Since DMV was not included in the EGRI assessment, and obstructive sleep apnea (OSA) is also correlated with DMV, a study correlating the prediction of DA and OSA (identified by STOP-Bang questionnaire, SB) seemed important. METHODS: We accessed a database previously collected for a post analysis simulation of the airway difficulty predictivity of the EGRI, associated with normal and difficult airway, particularly DMV. As secondary aim, we measured the correlation between the SB prediction system and DA, compared to the EGRI. RESULTS: A total of 2747 patients were included in the study. The proportion of patients with DI was 14.7% (95% CI 13.4-16) and the proportion of patients with DMV was 3.42% (95% CI 2.7-4.1). The incidence of DMV combined with DI was (2.3%). The optimal cutoff value of EGRI was 3. EGRI registered also an higher ability to predict DMV (AUC=0.76 (95% CI 0.71-0.81)). Adding the SB variables in the logistic model, the AUC increases with the inclusion of "observed apnea" variable (0.83 vs. 0.81, p=0.03). The area under the ROC curve for the patients with DI and DMV was 0.77 (95% CI 0.72-0.83). CONCLUSIONS: This study confirms that the incidence of DA is not negligible and suggests the use of the EGRI as simple bedside predictive score to improve patient safety.