Objective. This study evaluates the natural course of hearing loss (HL) prior to treatment in patients with progressive tumors and an indication for active intervention. Evaluating this patient... Show moreObjective. This study evaluates the natural course of hearing loss (HL) prior to treatment in patients with progressive tumors and an indication for active intervention. Evaluating this patient group specifically can put hearing outcomes after vestibular schwannoma therapy into an adequate context.Study Design. Retrospective cohort study.Setting. Tertiary referral center.Methods. Inclusion criteria comprised unilateral vestibular schwannomas prior to active treatment, with >= 2 mm extracanalicular (EC) tumor growth and >= 2 audiograms. We performed a comprehensive assessment of hearing using multiple outcome parameters including (the annual decrease in) pure-tone averages (PTAs; an average of 0.5, 1, 2, and 3 kHz). Predictors for HL were evaluated (patient age, tumor size/progression, follow-up duration, baseline hearing).Results. At presentation, 86% of patients suffered from sensorineural HL on the affected side (>= 20 dB PTA) with a median of 39 dB (interquartile rate [IQR]: 27-51 dB). The median follow-up duration was 21 months (IQR: 13-34 months), after which 58% (187/322) of patients experienced progressive HL (>= 10 dB), with a median increase of 6.4 dB/year. At the last follow-up, the median PTA was 56 dB (IQR: 37-73). Median speech discrimination scores deteriorated from 90% (IQR: 70%-100%) to 65% (IQR: 35%-100%). Tumor progression (maximal EC diameter) was significantly correlated to the progression of sensorineural HL, corrected for follow-up (F(2,228) = 10.4, p < .001, R-2 = 8%).Conclusion. The majority of patients (58%) with radiologically confirmed progressive vestibular schwannomas experience progressive sensorineural HL during observation. Tumor progression rate, EC tumor extension, and longer follow-up are factors associated with more sensorineural HL. Show less
Background: SARS-CoV-2 is straining health care systems globally. The burden on hospitals during the pandemic could be reduced by implementing prediction models that can discriminate patients who... Show moreBackground: SARS-CoV-2 is straining health care systems globally. The burden on hospitals during the pandemic could be reduced by implementing prediction models that can discriminate patients who require hospitalization from those who do not. The COVID-19 vulnerability (C-19) index, a model that predicts which patients will be admitted to hospital for treatment of pneumonia or pneumonia proxies, has been developed and proposed as a valuable tool for decision-making during the pandemic. However, the model is at high risk of bias according to the "prediction model risk of bias assessment" criteria, and it has not been externally validated.Objective: The aim of this study was to externally validate the C-19 index across a range of health care settings to determine how well it broadly predicts hospitalization due to pneumonia in COVID-19 cases.Methods: We followed the Observational Health Data Sciences and Informatics (OHDSI) framework for external validation to assess the reliability of the C-19 index. We evaluated the model on two different target populations, 41,381 patients who presented with SARS-CoV-2 at an outpatient or emergency department visit and 9,429,285 patients who presented with influenza or related symptoms during an outpatient or emergency department visit, to predict their risk of hospitalization with pneumonia during the following 0-30 days. In total, we validated the model across a network of 14 databases spanning the United States, Europe, Australia, and Asia.Results: The internal validation performance of the C-19 index had a C statistic of 0.73, and the calibration was not reported by the authors. When we externally validated it by transporting it to SARS-CoV-2 data, the model obtained C statistics of 0.36, 0.53 (0.473-0.584) and 0.56 (0.488-0.636) on Spanish, US, and South Korean data sets, respectively. The calibration was poor, with the model underestimating risk. When validated on 12 data sets containing influenza patients across the OHDSI network, the C statistics ranged between 0.40 and 0.68.Conclusions: Our results show that the discriminative performance of the C-19 index model is low for influenza cohorts and even worse among patients with COVID-19 in the United States, Spain, and South Korea. These results suggest that C-19 should not be used to aid decision-making during the COVID-19 pandemic. Our findings highlight the importance of performing external validation across a range of settings, especially when a prediction model is being extrapolated to a different population. In the field of prediction, extensive validation is required to create appropriate trust in a model. Show less
Tak, E.C.P.M.; Hespen, A.T.H. van; Verhaak, P.F.M.; Eekhof, J.; Hopman-Rock, M. 2016
