Prolonged Itraconazole Therapy as Sole Treatment for Patients with Allergic Fungal Rhinosinusitis

BACKGROUND

Currently, the mainstay of treatment for allergic fungal rhinosinusitis (AFRS) is surgical debridement along with topical or systemic steroids. However, prolonged systemic steroid therapy comes with side effects and is also sometimes contraindicated. Systemic antifungals have been used earlier as an adjunct to steroids or in refractory cases, but they have not been used as the sole primary treatment.

OBJECTIVE

To study the effectiveness of sole Itraconazole therapy in patients with AFRS by comparison of clinical, radiological, and biochemical parameters before and after treatment.

METHODS

Thirty-four patients diagnosed with localized sino-nasal AFRS were recruited and started on the tablet Itraconazole 200 mg orally twice daily for 3 months with q2weekly monitoring of liver function tests. The baseline clinical, radiological, and biochemical parameters were then compared with those after completion of 3 months of Itraconazole therapy.

RESULTS

There was significant difference between all the parameters-clinical: SNOT-22 score (p < 0.001) and Meltzer endoscopy score (p < 0.001), radiological: Lund-Mackay score (p = 0.004) and 20-point CT score (p = 0.002), and biochemical: serum total IgE (p < 0.001), Aspergillus-specific IgE (p < 0.001), and absolute eosinophil count (p < 0.001). The clearance of the disease was more in anterior sinuses than the posterior ones.

CONCLUSION

Prolonged Itraconazole can be given as sole therapy in AFRS, especially in patients for whom steroids are contraindicated or in those who are awaiting surgery. It can result in symptomatic and radiological improvement, but surgery still remains the definitive treatment option for AFRS for complete clearance of disease.

LEVEL OF EVIDENCE

3 Laryngoscope, 134:545-551, 2024.

In-vivo lymphoscintigraphy of sinonasal tumors identifies retropharyngeal node and level I as predominant sentinel nodes

OBJECTIVE

To evaluate by in- vivo lymphoscintigraphy and SPECT-CT imaging, the lymphatic drainage patterns of para-nasal sinus(PNS) tumors. To confirm or refute the belief of the retropharyngeal lymph node (RPLN) being the significant draining lymph node for such tumors.

METHODS

Prospective cohort study conducted on previously untreated PNS tumors with no clinico-radiological evidence of lymph node metastasis. Lymphoscintigraphy undertaken by nasal endoscopic assisted peritumoral injection of 99mTc Sulfur colloid. Injections were classified as anterior or posterior as per a vertical line along the maxillary sinus ostium.

RESULTS

17 patients were included. Lymphoscintigraphy successfully identified 17 sentinel nodes in 15 patients and was unsuccessful (lymphoscintigraphy failure) in 2 patients. Predominant sites of sentinel lymphatic drainage were noted to be the RPLN (n = 8; 47%), and Level I (n = 7; 42%). Occasional drainage was identified at the peri-parotid node(n = 1) and at Level II (n = 1). Contralateral drainage was noted in 2 patients (level I-1 and RPLN-1). Anterior injections drained predominantly to Level I (6/8) and RPLN (2/8), while posterior injections drained predominantly to the RPLN ( 6/7). The relative risk of RPLN being identified as the sentinel node was significantly higher for posteriorly placed injections than for anteriorly placed injections (RR- 3.43; 95% CI-1.0-11.8, p = 0.05).

CONCLUSION

The RPLN is noted as a frequent draining node for sino-nasal tumours and merits routine attention in all sino-nasal tumors. The radio-colloid SPECT-CT technique described here offers an excellent in-vivo technique to further explore and validate the lymphatic drainage pathways of these tumours.

Effect of surgical intervention on serum insulin-like growth factor 1 in patients with obstructive sleep apnoea

OBJECTIVE

To evaluate the effect of surgical intervention on serum insulin-like growth factor 1 levels in patients with obstructive sleep apnoea.

METHODS

A prospective study was conducted in a tertiary care hospital of adult patients with obstructive sleep apnoea for whom continuous positive airway pressure therapy failed or was refused. All patients underwent polysomnography and serum insulin-like growth factor 1 evaluation pre-operatively and at three months post-operatively. The site of surgery was determined using Müller's manoeuvre and ApneaGraph AG 200.

RESULTS

Fifteen patients were included with a mean age of 38 years: 11 males and 4 females. The mean pre-operative Apnoea-Hypopnoea Index using polysomnography was 53.7 events per hour, and the mean post-operative Apnoea-Hypopnoea Index at three months was 15.3 events per hour (p = 0.0001). The mean pre-operative serum insulin-like growth factor 1 was 160.2 μg/l, while the mean post-operative value was 236.98 μg/l (p = 0.005).

CONCLUSION

In adult patients with obstructive sleep apnoea for whom continuous positive airway pressure therapy fails, site-specific surgical intervention to treat the obstruction leads to an increase in serum insulin-like growth factor 1 levels.

Discriminatively Trained Latent Ordinal Model for Video Classification

We address the problem of video classification for facial analysis and human action recognition. We propose a novel weakly supervised learning method that models the video as a sequence of automatically mined, discriminative sub-events (e.g., onset and offset phase for "smile", running and jumping for "highjump"). The proposed model is inspired by the recent works on Multiple Instance Learning and latent SVM/HCRF - it extends such frameworks to model the ordinal aspect in the videos, approximately. We obtain consistent improvements over relevant competitive baselines on four challenging and publicly available video based facial analysis datasets for prediction of expression, clinical pain and intent in dyadic conversations, and on three challenging human action datasets. We also validate the method with qualitative results and show that they largely support the intuitions behind the method.

Automated Assessment of Children's Postoperative Pain Using Computer Vision

BACKGROUND

Current pain assessment methods in youth are suboptimal and vulnerable to bias and underrecognition of clinical pain. Facial expressions are a sensitive, specific biomarker of the presence and severity of pain, and computer vision (CV) and machine-learning (ML) techniques enable reliable, valid measurement of pain-related facial expressions from video. We developed and evaluated a CVML approach to measure pain-related facial expressions for automated pain assessment in youth.

METHODS

A CVML-based model for assessment of pediatric postoperative pain was developed from videos of 50 neurotypical youth 5 to 18 years old in both endogenous/ongoing and exogenous/transient pain conditions after laparoscopic appendectomy. Model accuracy was assessed for self-reported pain ratings in children and time since surgery, and compared with by-proxy parent and nurse estimates of observed pain in youth.

RESULTS

Model detection of pain versus no-pain demonstrated good-to-excellent accuracy (Area under the receiver operating characteristic curve 0.84-0.94) in both ongoing and transient pain conditions. Model detection of pain severity demonstrated moderate-to-strong correlations (r = 0.65-0.86 within; r = 0.47-0.61 across subjects) for both pain conditions. The model performed equivalently to nurses but not as well as parents in detecting pain versus no-pain conditions, but performed equivalently to parents in estimating pain severity. Nurses were more likely than the model to underestimate youth self-reported pain ratings. Demographic factors did not affect model performance.

CONCLUSIONS

CVML pain assessment models derived from automatic facial expression measurements demonstrated good-to-excellent accuracy in binary pain classifications, strong correlations with patient self-reported pain ratings, and parent-equivalent estimation of children's pain levels over typical pain trajectories in youth after appendectomy.

Classification and Weakly Supervised Pain Localization using Multiple Segment Representation

Automatic pain recognition from videos is a vital clinical application and, owing to its spontaneous nature, poses interesting challenges to automatic facial expression recognition (AFER) research. Previous pain vs no-pain systems have highlighted two major challenges: (1) ground truth is provided for the sequence, but the presence or absence of the target expression for a given frame is unknown, and (2) the time point and the duration of the pain expression event(s) in each video are unknown. To address these issues we propose a novel framework (referred to as MS-MIL) where each sequence is represented as a bag containing multiple segments, and multiple instance learning (MIL) is employed to handle this weakly labeled data in the form of sequence level ground-truth. These segments are generated via multiple clustering of a sequence or running a multi-scale temporal scanning window, and are represented using a state-of-the-art Bag of Words (BoW) representation. This work extends the idea of detecting facial expressions through 'concept frames' to 'concept segments' and argues through extensive experiments that algorithms such as MIL are needed to reap the benefits of such representation. The key advantages of our approach are: (1) joint detection and localization of painful frames using only sequence-level ground-truth, (2) incorporation of temporal dynamics by representing the data not as individual frames but as segments, and (3) extraction of multiple segments, which is well suited to signals with uncertain temporal location and duration in the video. Extensive experiments on UNBC-McMaster Shoulder Pain dataset highlight the effectiveness of the approach by achieving competitive results on both tasks of pain classification and localization in videos. We also empirically evaluate the contributions of different components of MS-MIL. The paper also includes the visualization of discriminative facial patches, important for pain detection, as discovered by our algorithm and relates them to Action Units that have been associated with pain expression. We conclude the paper by demonstrating that MS-MIL yields a significant improvement on another spontaneous facial expression dataset, the FEEDTUM dataset.