METHODS: Anonymised data consisting of 44 independent predictor variables from 355 adults diagnosed with COVID-19, at a UK hospital, was manually extracted from electronic patient records for retrospective, case-control analysis. Primary outcomes included inpatient mortality, required ventilatory support, and duration of inpatient treatment. Pulmonary embolism sequala was the only secondary outcome. After balancing data, key variables were feature selected for each outcome using random forests. Predictive models were then learned and constructed using Bayesian networks.
RESULTS: The proposed probabilistic models were able to predict, using feature selected risk factors, the probability of the mentioned outcomes. Overall, our findings demonstrate reliable, multivariable, quantitative predictive models for four outcomes, which utilise readily available clinical information for COVID-19 adult inpatients. Further research is required to externally validate our models and demonstrate their utility as risk stratification and clinical decision-making tools.
METHODS: Incidence of thigh pain was lower in 2008 compared to 2006 and 2007 (p < 0.0001). The percentage of patients requiring blood transfusions (p = 0.09), duration of IDC ≥ 7 days (p = 0.27), wound dehiscence and re-operation rate were lower in 2008 in contrast to 2006 and 2007 (p = 0.43). Only 209 patients (82.3%) were available for review at 1 year. There were two (1.0%) cases of recurrent vault prolapse.
RESULTS: The subjective and objective cure rates at 1 year after this mesh implant surgery in 2006, 2007 and 2008 were 92.1% and 92.1%; 97.0% and 92.4% and 100% and 97%, respectively. The mesh erosion rate was remarkably lower in 2008 as compared to 2007 and 2006 (p < 0.001).
CONCLUSIONS: This synthetic mesh-augmented implant surgery is effective and safe, and surgical outcome appears related to the learning curve of the surgeon.
METHODS: This is a retrospective observational study involving 58 pregnancies from 1st January 2013 to 31st December 2019. Inclusion criteria were previous mid-trimester miscarriage and/or preterm birth, previous cervical surgery or short cervical length on routine sonogram. The demographic data, characteristics of each pregnancy and details of outcomes and management were described.
RESULTS: The majority of women were Malay with mean age and body mass index of 32.9 ± 4.2 years and 27.1 ± 6.3 kg/m2 respectively. The most frequent indications for Arabin pessary insertion were previous mid-trimester miscarriage (46.4%) and early preterm birth (17.2%). A total of 73.4% of these women had the pessary inserted electively at a mean cervical length of 31.6 ± 9.1 mm at median gestation of 15.0 weeks. They were managed as outpatient (56.9%), inpatient (24.1%) or mixed (19.0%) with combination of progestogen (81.0%) and 53.4% received antenatal corticosteroids. Spontaneous preterm birth at or more than 34 weeks gestation occurred in 74.1% with birthweight at or more than 2000 g (82.4%). Despite cervical funneling in 12 women (20.7%), 66.7% delivered at or later than 34 weeks gestation and 2 (16.7%) resulted in miscarriage.
CONCLUSIONS: Insertion of the Arabin pessary is beneficial to prevent spontaneous preterm birth in pregnant women who are at high risk. In particular, early insertion and close monitoring allows the best possible outcomes.
TRIAL REGISTRATION: This study was retrospectively registered with ClinicalTrials.gov ( NCT04638023 ) on 20/11/2020.
METHODS: A retrospective cohort analysis of patients aged ≥12 years, diagnosed with an ALRTI in primary care in 2014-15 was conducted using data from the Clinical Practice Research Datalink. Current asthma status, asthma medication and oral antibiotic use within 3 days of ALRTI infection was determined. Treatment frequency was calculated by asthma status. Mixed-effect regression models were used to explore between-practice variation and treatment determinants.
RESULTS: There were 127,976 ALRTIs reported among 110,418 patients during the study period, of whom 17,952 (16%) had asthma. Respectively, 81 and 79% of patients with and without asthma received antibiotics, and 41 and 15% asthma medication. There were significant differences in between-practice prescribing for all treatments, with greatest differences seen for oral steroids (odds ratio (OR) 18; 95% CI 7-82 and OR = 94; 33-363, with and without asthma) and asthma medication only (OR 7; 4-18 and OR = 17; 10-33, with and without asthma). Independent predictors of antibiotic prescribing among patients with asthma included fewer previous ALRTI presentations (≥2 vs. 0 previous ALRTI: OR = 0.25; 0.16-0.39), higher practice (OR = 1.47; 1.35-1.60 per SD) and prior antibiotic prescribing (3+ vs. 1 prescriptions OR = 1.28; 1.04-1.57) and concurrent asthma medication (OR = 1.44; 1.32-1.57). Independent predictors of asthma medication in patients without asthma included higher prior asthma medication prescribing (≥7 vs. 0 prescriptions OR = 2.31; 1.83-2.91) and concurrent antibiotic prescribing (OR = 3.59; 3.22-4.01).
CONCLUSION: Findings from the study indicate that antibiotics are over-used for ALRTI, irrespective of asthma status, and asthma medication is over-used in patients without asthma, with between-practice variation suggesting considerable clinical uncertainty. Further research is urgently needed to clarify the role of these medications for ALRTI.
DESIGN: Artificial intelligence (neural network) study.
METHODS: We assessed 1400 OCT scans of patients with neovascular AMD. Fifteen physical features for each eligible OCT, as well as patient age, were used as input data and corresponding recorded visual acuity as the target data to train, validate, and test a supervised neural network. We then applied this network to model the impact on acuity of defined OCT changes in subretinal fluid, subretinal hyperreflective material, and loss of external limiting membrane (ELM) integrity.
RESULTS: A total of 1210 eligible OCT scans were analyzed, resulting in 1210 data points, which were each 16-dimensional. A 10-layer feed-forward neural network with 1 hidden layer of 10 neurons was trained to predict acuity and demonstrated a root mean square error of 8.2 letters for predicted compared to actual visual acuity and a mean regression coefficient of 0.85. A virtual model using this network demonstrated the relationship of visual acuity to specific, programmed changes in OCT characteristics. When ELM is intact, there is a shallow decline in acuity with increasing subretinal fluid but a much steeper decline with equivalent increasing subretinal hyperreflective material. When ELM is not intact, all visual acuities are reduced. Increasing subretinal hyperreflective material or subretinal fluid in this circumstance reduces vision further still, but with a smaller gradient than when ELM is intact.
CONCLUSIONS: The supervised machine learning neural network developed is able to generate an estimated visual acuity value from OCT images in a population of patients with AMD. These findings should be of clinical and research interest in macular degeneration, for example in estimating visual prognosis or highlighting the importance of developing treatments targeting more visually destructive pathologies.
METHODS: C0 were retrieved from a large neonatal vancomycin dataset. Individual estimates of AUC0-24 were obtained from Bayesian post hoc estimation. Various ML algorithms were used for model building to C0 and AUC0-24. An external dataset was used for predictive performance evaluation.
RESULTS: Before starting treatment, C0 can be predicted a priori using the Catboost-based C0-ML model combined with dosing regimen and nine covariates. External validation results showed a 42.5% improvement in prediction accuracy by using the ML model compared with the population pharmacokinetic model. The virtual trial showed that using the ML optimized dose; 80.3% of the virtual neonates achieved the pharmacodynamic target (C0 in the range of 10-20 mg/L), much higher than the international standard dose (37.7-61.5%). Once therapeutic drug monitoring (TDM) measurements (C0) in patients have been obtained, AUC0-24 can be further predicted using the Catboost-based AUC-ML model combined with C0 and nine covariates. External validation results showed that the AUC-ML model can achieve an prediction accuracy of 80.3%.
CONCLUSION: C0-based and AUC0-24-based ML models were developed accurately and precisely. These can be used for individual dose recommendations of vancomycin in neonates before treatment and dose revision after the first TDM result is obtained, respectively.
METHODS: Thirty patients who underwent distal volar locking plate for distal radius fracture were included in a retrospective study. In all 30 patients no dorsal and intra-articular screw penetration were detected on standard AP and lateral views of a plain radiograph. CT scan of the operated wrist was performed to determine the number of intra-articular and dorsal screw penetrations. Clinical examination was performed to determine the wrist functions in comparison to the normal wrist.
RESULTS: Nineteen wrists were noted to have screw penetration either dorsally or intraarticularly. The highest incidence is in the 2nd extensor compartment where 13 screws had penetrated with a mean of 2.46 mm. Six screws penetrated into the distal radial ulnar joint and five screws into the wrist joint with a mean of 2.83 mm and 2.6 mm, respectively. However, there was no incidence of tendon irritation or rupture.
CONCLUSIONS: This study demonstrated a high incidence of dorsal and intra-articular screw penetration detected by CT scan which was not apparent in plain radiograph. We recommend that surgeons adhere to the principle of only near cortex fixation and downsizing the locking screw length by 2 mm.
MATERIALS AND METHODS: This is a cross-sectional, retrospective study design. All patients who received vildagliptin in the Pharmacy Integrated Health System (PHIS) registry database from 2016 to 2021 were included as study samples. The exclusion criteria were being less than 18 years old and having type 1 diabetes mellitus. Patients' medical records were retrieved after sampling, and data were collected. One medical record was missing, thus SPSS analysis were performed on 144 vildagliptin users.
RESULTS: In total, 84 females (58.3%) and 60 males (41.7%) with a mean age of 62.1 (±10.1) years were analysed in this study. Mean HbA1c pre-therapy was 8.5 ± 2.1%; while posttherapy 6 months demonstrated a mean HbA1c of 7.9 ± 1.8%. Use of vildagliptin alone or as an adjunct was associated with a mean reduction of 0.6% in HbA1c (p = 0.01). Factors influencing this HbA1c reduction were advancing age, specifically individuals aged 62 years and older (p = 0.02), patients who are already receiving insulin therapy (p=0.00) and those who express a willingness to commence insulin treatment during the counselling session prior to initiating the treatment plan (p = 0.00). Reasons for vildagliptin initiation documented by prescribers were non-insulin acceptance (n = 59, 40.97%), frequent hypoglycaemia (n = 6, 4.1%) and non-compliance with medications (n = 23, 15.9%). There was no association between demographic, medical background and reason for starting vildagliptin variables and HbA1c reduction (p < 0.001).
CONCLUSION: This study showed that initiating vildagliptin alone or as an adjunct therapy significantly reduced HbA1c and is beneficial for uncontrolled diabetes patients. While advancing age, concurrent administration of insulin and the patients' willingness to accept insulin treatment prior to the commencement of therapy were the factors that influenced HbA1c reduction among patients receiving vildagliptin therapy, we recommend primary care providers prioritise all of the significant variables discovered before initiating vildagliptin for their patients.