Screening accuracy and cut-offs of the Polish version of Communication and Symbolic Behavior Scales-Developmental Profile Infant-Toddler Checklist.

BACKGROUND: The first stage of diagnosing autism spectrum disorders usually involves population screening to detect children at risk. This study aims to assess the predictive convergent validity of the Polish version of the Communication and Symbolic Behavior Scales-Developmental Profile Infant-Toddler Checklist (CSBS-DP ITC) with the Autism Spectrum Rating Scales (ASRS), evaluate its sensitivity and specificity and assess the cut-off points for the possibility of using this questionnaire in population screening among children aged 6 to 24 months. METHOD: The study was conducted among 602 children from the general population who had previously participated in the earlier phase of validation of the questionnaire for Polish conditions. The collected data were statistically processed to calculate the accuracy (i.e. sensitivity, specificity) of the questionnaire. RESULTS: In individual age groups, the sensitivity of the questionnaire varies from 0.667 to 0.750, specificity from 0.854 to 0.939, positive predictive value from 0.261 to 0.4 and negative predictive value-from 0.979 to 0.981. Screening accuracy ranges from 0.847 to 0.923 depending on the age group. The adopted cut-off points are 21 points for children aged 9-12 months, 36 for children aged 13-18 months, 39 for children aged 19-24 months. Cut-off points could not be established for children aged 6-8 months. The convergent validity values with the ASRS ranged from -0.28 to -0.431 and were highest in the group of the oldest children. CONCLUSIONS: These results indicate that the Polish version of the CSBS-DP ITC can be used as an effective tool for ASD universal screening.

Prevalence of headaches and their relationship with obstructive sleep apnea (OSA) - Systematic review and meta-analysis.

Obstructive sleep apnea (OSA) is one of the most common sleep disorders; however, there are inconsistent results about the connection and occurrence of primary and secondary headaches in OSA. Therefore, the primary objectives were to estimate the prevalence and potential relationship between all types of headaches and OSA. A systematic review was conducted according to PRISMA 2020 guidelines. Studies were searched in PubMed, Embase, and Web of science up to July 2023. The Joanna Briggs Institute tool assessed the risk of bias. 1845 articles were identified, and 23 studies describing 15,402 patients were included. Pooled prevalence of all headaches in OSA was 33% (95% CI: 0.25-0.41), 33% for morning headaches (95% CI: 0.24-0.45), 25% for sleep apnea headaches (95% CI: 0.18-0.34), 19% for tension-type headache (95% CI: 0.15-0.23), and 16% for migraine (95% CI: 0.09-0.26). Relative risk for the occurrence of headache in OSA patients compared to the non-OSA people was 1.43 (95% CI: 0.92-2.25). OSA females and males had morning headaches with similar frequency. The prevalence of headaches in OSA was moderate. OSA did not increase the risk of headache. There is a need to conduct further studies focused on bidirectional connections between sleep disorders and headaches.

Liver Function Tests in COVID-19: Assessment of the Actual Prognostic Value.

Deviations in laboratory tests assessing liver function in patients with COVID-19 are frequently observed. Their importance and pathogenesis are still debated. In our retrospective study, we analyzed liver-related parameters: aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP), gamma-glutamyltransferase (GGT), total bilirubin (TBIL), albumin, comorbidities and other selected potential risk factors in patients admitted with SARS-CoV-2 infection to assess their prognostic value for intensive care unit admission, mechanical ventilation necessity and mortality. We compared the prognostic effectiveness of these parameters separately and in pairs to the neutrophil-to-lymphocyte ratio (NLR) as an independent risk factor of in-hospital mortality, using the Akaike Information Criterion (AIC). Data were collected from 2109 included patients. We created models using a sample with complete laboratory tests n = 401 and then applied them to the whole studied group excluding patients with missing singular variables. We estimated that albumin may be a better predictor of the COVID-19-severity course compared to NLR, irrespective of comorbidities (p < 0.001). Additionally, we determined that hypoalbuminemia in combination with AST (OR 1.003, p = 0.008) or TBIL (OR 1.657, p = 0.001) creates excellent prediction models for in-hospital mortality. In conclusion, the early evaluation of albumin levels and liver-related parameters may be indispensable tools for the early assessment of the clinical course of patients with COVID-19.