Video-telehealth to support clinical assessment and management of acutely unwell older people in Residential Aged Care: a pre-post intervention study

Background Older people living in Residential Aged Care (RAC) are at high risk of clinical deterioration. Telehealth has the potential to provide timely, patient-centred care where transfer to hospital can be a burden and avoided. The extent to which video telehealth is superior to other forms of telecommunication and its impact on management of acutely unwell residents in aged care facilities has not been explored previously. Methods In this study, video-telehealth consultation was added to an existing program, the Aged Care Emergency (ACE) program, aiming at further reducing Emergency Department (ED) visits and hospital admissions. This controlled pre-post study introduced video-telehealth consultation as an additional component to the ACE program for acutely unwell residents in RACs. Usual practice is for RACs and ACE to liaise via telephone. During the study, when the intervention RACs called the ED advanced practice nurse, video-telehealth supported clinical assessment and management. Five intervention RACs were compared with eight control RACs, all of whom refer to one community hospital in regional New South Wales, Australia. Fourteen months pre-video-telehealth was compared with 14 months post-video-telehealth using generalized linear mixed models for hospital admissions after an ED visit and ED visits. One thousand two hundred seventy-one ED visits occurred over the 28-month study period with 739 subsequent hospital admissions. Results There were no significant differences in hospital admission or ED visits after the introduction of video-telehealth; adjusted incident rate ratios (IRR) were 0.98 (confidence interval (CI) 0.55 to 1.77) and 0.89 (95% CI 0.53 to 1.47) respectively. Conclusions Video-telehealth did not show any incremental benefit when added to a structured hospital avoidance program with nursing telephone support. Trial registration The larger Aged Care Emergency evaluation is registered with ANZ Clinical Trials Registry, ACTRN12616000588493.

Integrated Clinical and Genomic Models to Predict Optimal Cytoreduction in High Grade Serous Ovarian Cancer

Background: Advanced high grade serous (HGSC) ovarian cancer is treated with either primary surgery followed by chemotherapy or neoadjuvant chemotherapy followed by interval surgery. The decision to proceed with surgery either primarily or after chemotherapy is based on a surgeon’s clinical assessment and prediction of an optimal outcome. Optimal surgery is correlated with improved overall survival. This clinical assessment results in an optimal surgery approximately 70% of the time. We hypothesize that this prediction can be improved by using biological tumor data to predict optimal cytoreduction.Methods: With access to a large biobank of ovarian cancer tumors, we obtained genomic data on 83 patients encompassing gene expression, exon expression, long non-coding RNA, micro RNA, single nucleotide variants, copy number variation, DNA methylation, and fusion transcripts. We then used machine learning to incorporate this data with pre-operative clinical information to create predictive models which successfully predicted whether or not a patient’s cytoreductive surgery would have an optimal outcome. These models were then validated within The Cancer Genome Atlas (TCGA) HGSC database. Results: Of the 124 models created and validated, 21 performed at least equal if not better than our historical clinical rate of optimal debulking in advanced-stage HGSC as a control, 78%. Conclusions: This is the first time tumor genomic data has been used to predict surgical outcome in ovarian cancer. Prospective validation of these models could result in improving our ability to objectively predict which patients will undergo optimal cytoreduction and, therefore, improve our ovarian cancer outcomes.

The Application of Artificial Intelligence-Assisted Colposcopy in a Tertiary Care Hospital within a Cervical Pathology Diagnostic Unit

The rising global incidence of cervical cancer is estimated to have affected more than 600,000 women, and nearly 350,000 women are predicted to have died from the disease in 2020 alone. Novel advances in cancer prevention, screening, diagnosis and treatment have all but reduced the burden of cervical cancer in developed nations. Unfortunately, cervical cancer is still the number one gynecological cancer globally. A limiting factor in managing cervical cancer globally is access to healthcare systems and trained medical personnel. Any methodology or procedure that may simplify or assist cervical cancer screening is desirable. Herein, we assess the use of artificial intelligence (AI)-assisted colposcopy in a tertiary hospital cervical diagnostic pathology unit. The study group consisted of 48 women (mean age 34) who were referred to the clinic for a routine colposcopy by their gynecologist. Cervical images were taken by an EVA-Visualcheck TM colposcope and run through an AI algorithm that gave real-time binary results of the cervical images as being either normal or abnormal. The primary endpoint of the study assessed the AI algorithm’s ability to correctly identify histopathology results of CIN2+ as being abnormal. A secondary endpoint was a comparison between the AI algorithm and the clinical assessment results. Overall, we saw lower sensitivity of AI (66.7%; 12/18) compared with the clinical assessment (100%; 18/18), and histopathology results as the gold standard. The positive predictive value (PPV) was comparable between AI (42.9%; 12/28) and the clinical assessment (41.8%; 18/43). The specificity, however, was higher in the AI algorithm (46.7%; 14/30) compared to the clinical assessment (16.7%; 5/30). Comparing the congruence between the AI algorithm and histopathology results showed agreement 54.2% of the time and disagreement 45.8% of the time. A trained colposcopist was in agreement 47.9% and disagreement 52.1% of the time. Assessing these results, there is currently no added benefit of using the AI algorithm as a tool of speeding up diagnosis. However, given the steady improvements in the AI field, we believe that AI-assisted colposcopy may be of use in the future.