OBJECTIVE: To develop a nomogram for predicting occurrence of secondary pulmonary infection in patients with critically traumatic brain injury (TBI) during their stay in the intensive care unit, to further optimise personalised treatment for patients and support the development of effective, evidence-based prevention and intervention strategies.
METHODS: This study used patient data from the publicly available MIMIC-IV (Medical Information Mart for Intensive Care IV) database.
METHODS: A population-based retrospective cohort study.
METHODS: In this retrospective cohort study, 1780 patients with TBI were included and randomly divided into a training set (n=1246) and a development set (n=534). The impact of pulmonary infection on survival was analysed using Kaplan-Meier curves. A univariate logistic regression model was built in training set to identify potential factors for pulmonary infection, and independent risk factors were determined in a multivariate logistic regression model to build nomogram model. Nomogram performance was assessed with receiver operating characteristic (ROC) curves, calibration curves and Hosmer-Lemeshow test, and predictive value was assessed by decision curve analysis (DCA).
RESULTS: This study included a total of 1780 patients with TBI, of which 186 patients (approximately 10%) developed secondary lung infections, and 21 patients died during hospitalisation. Among the 1594 patients who did not develop lung infections, only 85 patients died (accounting for 5.3%). The survival curves indicated a significant survival disadvantage for patients with TBI with pulmonary infection at 7 and 14 days after intensive care unit admission (p<0.001). Both univariate and multivariate logistic regression analyses showed that factors such as race other than white or black, respiratory rate, temperature, mechanical ventilation, antibiotics and congestive heart failure were independent risk factors for pulmonary infection in patients with TBI (OR>1, p<0.05). Based on these factors, along with Glasgow Coma Scale and international normalised ratio variables, a training set model was constructed to predict the risk of pulmonary infection in patients with TBI, with an area under the ROC curve of 0.800 in the training set and 0.768 in the validation set. The calibration curve demonstrated the model\'s good calibration and consistency with actual observations, while DCA indicated the practical utility of the predictive model in clinical practice.
CONCLUSIONS: This study established a predictive model for pulmonary infections in patients with TBI, which may help clinical doctors identify high-risk patients early and prevent occurrence of pulmonary infections.

BACKGROUND: Multidrug-resistant tuberculosis is a type of tuberculosis that is resistant to at least the first-line antituberculosis drugs namely, rifampicin and isoniazid. However, most of these studies were limited only to a single hospital. Therefore, this study aimed to identify the determinants of multidrug-resistant tuberculosis among adults undergoing treatment for tuberculosis in the Tigray region of Ethiopia.
METHODS: Hospital-based unmatched case-control study was conducted from 1 April 2019 to 30 June 2019. A simple random sampling method was used to select the required sample size. Variables at a p value less than 0.25 in bivariate analysis were entered into a multivariable analysis to identify the determinant factors of multidrug-resistant tuberculosis. Finally, the level of significance was declared at p<0.05.
RESULTS: Rural residence (adjusted OR (AOR) 2.54; 95% CI 1.34 to 4.83), HIV (AOR 4.5; 95% CI 1.4 to 14.2), relapse (AOR 3.86; 95% CI 1.98 to 7.5), return after lost follow-up (AOR 6.29; 95% CI 1.64 to 24.2), treatment failure (AOR 5.87; 95% CI 1.39 to 24.8) were among the determinants of multidrug-resistant tuberculosis.
CONCLUSIONS: Rural residence, HIV, relapses, return after lost follow-up and treatment failure were the identified determinant factors of multidrug-resistance tuberculosis.

Pulmonary non-tuberculous mycobacterial (PNTM) disease has increased over the past several decades, especially in older women. Abnormal mucociliary clearance and abnormal nasal nitric oxide (nNO) have been associated with PNTM disease in other patient cohorts. Mucociliary clearance can be affected by NO-cyclic guanosine monophosphate signalling and, therefore, modulation of the pathway may be possible with phosphodiesterase inhibitors such as sildenafil as a novel therapeutic approach.
To define ex vivo characteristics of PNTM disease affected by sildenafil.
Subjects with PNTM infections were recruited into an open-label dose-escalation trial of sildenafil. Laboratory measurements and mucociliary measurements-ciliary beat frequency, nNO and 24-hour sputum production-were collected throughout the study period. Patients received sildenafil daily during the study period, with escalation from 20 to 40 mg three times per day.
Increased ciliary beat frequency occurred after a single dose of 40 mg sildenafil and after extended dosing of 40 mg sildenafil. The increase ciliary beat frequency was not seen with 20 mg sildenafil dosing. There were no changes in sputum production, nNO production, Quality of Life-Bronchiectasis-NTM module (QOL-B-NTM) questionnaire or the St George\'s Respiratory Questionnaire during the study period.
Sildenafil, 40 mg, increased ciliary beat frequency acutely as well as with extended administration.