Development and internal validation of a clinical prediction model for serious complications after emergency laparotomy.

PURPOSE: Emergency laparotomy (EL) is a common operation with high risk for postoperative complications, thereby requiring accurate risk stratification to manage vulnerable patients optimally. We developed and internally validated a predictive model of serious complications after EL. METHODS: Data for eleven carefully selected candidate predictors of 30-day postoperative complications (Clavien-Dindo grade > = 3) were extracted from the HELAS cohort of EL patients in 11 centres in Greece and Cyprus. Logistic regression with Least Absolute Shrinkage and Selection Operator (LASSO) was applied for model development. Discrimination and calibration measures were estimated and clinical utility was explored with decision curve analysis (DCA). Reproducibility and heterogeneity were examined with Bootstrap-based internal validation and Internal-External Cross-Validation. The American College of Surgeons National Surgical Quality Improvement Program's (ACS-NSQIP) model was applied to the same cohort to establish a benchmark for the new model. RESULTS: From data on 633 eligible patients (175 complication events), the SErious complications After Laparotomy (SEAL) model was developed with 6 predictors (preoperative albumin, blood urea nitrogen, American Society of Anaesthesiology score, sepsis or septic shock, dependent functional status, and ascites). SEAL had good discriminative ability (optimism-corrected c-statistic: 0.80, 95% confidence interval [CI] 0.79-0.81), calibration (optimism-corrected calibration slope: 1.01, 95% CI 0.99-1.03) and overall fit (scaled Brier score: 25.1%, 95% CI 24.1-26.1%). SEAL compared favourably with ACS-NSQIP in all metrics, including DCA across multiple risk thresholds. CONCLUSION: SEAL is a simple and promising model for individualized risk predictions of serious complications after EL. Future external validations should appraise SEAL's transportability across diverse settings.

Prospective multicenter external validation of postoperative mortality prediction tools in patients undergoing emergency laparotomy.

BACKGROUND: Accurate preoperative risk assessment in emergency laparotomy (EL) is valuable for informed decision making and rational use of resources. Available risk prediction tools have not been validated adequately across diverse health care settings. Herein, we report a comparative external validation of four widely cited prognostic models. METHODS: A multicenter cohort was prospectively composed of consecutive patients undergoing EL in 11 Greek hospitals from January 2020 to May 2021 using the National Emergency Laparotomy Audit (NELA) inclusion criteria. Thirty-day mortality risk predictions were calculated using the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP), NELA, Portsmouth Physiological and Operative Severity Score for the Enumeration of Mortality and Morbidity (P-POSSUM), and Predictive Optimal Trees in Emergency Surgery Risk tools. Surgeons' assessment of postoperative mortality using predefined cutoffs was recorded, and a surgeon-adjusted ACS-NSQIP prediction was calculated when the original model's prediction was relatively low. Predictive performances were compared using scaled Brier scores, discrimination and calibration measures and plots, and decision curve analysis. Heterogeneity across hospitals was assessed by random-effects meta-analysis. RESULTS: A total of 631 patients were included, and 30-day mortality was 16.3%. The ACS-NSQIP and its surgeon-adjusted version had the highest scaled Brier scores. All models presented high discriminative ability, with concordance statistics ranging from 0.79 for P-POSSUM to 0.85 for NELA. However, except the surgeon-adjusted ACS-NSQIP (Hosmer-Lemeshow test, p = 0.742), all other models were poorly calibrated ( p < 0.001). Decision curve analysis revealed superior clinical utility of the ACS-NSQIP. Following recalibrations, predictive accuracy improved for all models, but ACS-NSQIP retained the lead. Between-hospital heterogeneity was minimum for the ACS-NSQIP model and maximum for P-POSSUM. CONCLUSION: The ACS-NSQIP tool was most accurate for mortality predictions after EL in a broad external validation cohort, demonstrating utility for facilitating preoperative risk management in the Greek health care system. Subjective surgeon assessments of patient prognosis may optimize ACS-NSQIP predictions. LEVEL OF EVIDENCE: Diagnostic Test/Criteria; Level II.

The implementation of the 'Testing of Phonological Skills (TOPS)' tool: a computer-based phonological analysis algorithm.

A computer-based algorithm was used for phonological analysis of Cypriot Greek (CG) word productions elicited from a sample of typically developing (TD) CG-speaking children. Specific focus was on (a) creating a complete CG phonetic and phonological test; (2) implementing a novel computer-based algorithm in the analysis of selected speech stimuli from CG-speaking TD children; and (3) examining independent and relational speech patterns based on the computer-based algorithm. The probe word list consisted of 182 target words and included all CG consonants in all word positions. Normative data were collected on a representative sample of 20 TD CG-speaking children aged 3;0 to 6;0 years. Productions were phonetically transcribed and inserted into the computer algorithm for automatic data reduction of measurable dependent variables including the percentage of consonants correct (PCC), phonetic inventory size (PIS), word-initial onset deletion (IOD), and Regressive Assimilation (RASS). Correlation analyses revealed significant interactions among phonetic and phonological patterns as well as predictive relationships of PCC and segmental word-position. The implementation of this specific probe word list permitted a homogeneous and inclusive sampling analysis. Participants presented specific developmental trajectories of segmental and phonological patterns as a function of age.

[« Forum Passerelle ¼: an opportunity for caregivers to meet the professionals of service and support in Alzheimer's disease and related diseases.] / « Forum Passerelle ¼ : une opportunité pour les proches aidants de rencontrer les professionnels des dispositifs d'aide et d'accompagnement dans le cadre de la maladie d'Alzheimer et maladies apparentées.

The aim of this study was to assess 1) the satisfaction of caregivers of patients with Alzheimer's disease or related diseases regarding a new collective support intervention called "Forum Passerelle"(Gateway forum), and 2) to assess the impact of "Forum Passerelle" on the implementation of services and support for patients or caregivers. This is an observational study carried out on the basis of 3 "Forum Passerelle" (between October 2018 and February 2020). Following each "Forum Passerelle", the caregivers' satisfaction was assessed and the number of services and support was counted. In total, 78 caregivers took part in these first "Forum passerelle". The satisfaction survey showed that 98% of caregivers were satisfied with the topics covered by "Forum Passerelle". The forum fully met the expectations of 61% of caregivers and partially for 39%. Among the caregivers contacted 3 or 6 months after the forum, 68% had set up a new service/support (85 new services/support for the sick relatives and 32 for caregivers) and 59% declared that these changes had taken place thanks to the "Forum Passerelle". To conclude, the first 3 "Forums Passerelle" were received very favorably by caregivers and contributed to the implementation of new services to optimize home support for the sick relative.