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Objective: This study aimed to identify predictors of brain CT abnormalities in patients who sustained a mild traumatic brain injury (mTBI). Methods: Retrospective observational cohort of adult patients with mTBI (Glasgow Coma Score 13–15) that occurred within the preceding 24 h. Results: 2548 (91%) of the cohort had a brain CT and 698 (27%) demonstrated abnormal findings. The most frequently observed CT abnormalities were bleeding (638, 25%) and fractures (190, 7.4%). Multivariate logistic regression analysis revealed several significant predictors associated with the presence of brain CT abnormalities including older age [P < 0.0001], male sex [P < 0.0001], loss of consciousness [P = 0.0041], associated vomiting [P = 0.0011], alteration of consciousness (AOC) [P = 0102], and GCS score [P < 0.0001]. This was a robust model with an R² of 14.2%. Conclusion: In this retrospective analysis, older age, male sex, the presence of loss of consciousness or alteration in consciousness, lower GCS score, and associated vomiting were found to be significant predictors of having an abnormal brain CT. These findings highlight the importance of considering these factors when determining the necessity of brain CT scans in patients with mTBI and suggest that existing clinical decision rules may be limited. These findings may also help to inform clinical decision rules. Early identification of individuals at a higher risk of CT abnormalities may assist in appropriate management and allocation of healthcare resources. [ABSTRACT FROM AUTHOR]

Prediction models help to target patients at risk of multidrug-resistant organism (MDRO) colonization or infection and could serve as tools informing clinical practices to prevent MDRO transmission and inappropriate empiric antibiotic therapy. However, there is limited evidence to identify which among the available models are of low risk of bias and suitable for clinical application. To identify, describe, appraise, and summarise the performance of all prognostic and diagnostic models developed or validated for predicting MDRO colonization or infection. Six electronic literature databases and clinical registration databases were searched until April 2022. Development and validation studies of any multivariable prognostic and diagnostic models to predict MDRO colonization or infection in adults. Adults (≥ 18 years old) without MDRO colonization or infection (in prognostic models) or with unknown or suspected MDRO colonization or infection (in diagnostic models). The Prediction Model Risk of Bias Assessment Tool was used to assess the risk of bias. Evidence certainty was assessed using the Grading of Recommendations Assessment, Development, and Evaluation approach. Meta-analyses were conducted to summarize the discrimination and calibration of the models' external validations conducted in at least two non-overlapping datasets. We included 162 models (108 studies) developed for diagnosing (n = 135) and predicting (n = 27) MDRO colonization or infection. Models exhibited a high-risk of bias, especially in statistical analysis. High-frequency predictors were age, recent invasive procedures, antibiotic usage, and prior hospitalization. Less than 25% of the models underwent external validations, with only seven by independent teams. Meta-analyses for one diagnostic and two prognostic models only produced very low to low certainty of evidence. The review comprehensively described the models for identifying patients at risk of MDRO colonization or infection. We cannot recommend which models are ready for application because of the high-risk of bias, limited validations, and low certainty of evidence from meta-analyses, indicating a clear need to improve the conducting and reporting of model development and external validation studies to facilitate clinical application. [ABSTRACT FROM AUTHOR]

Copyright of Revista de Educación e Investigación en Emergencias is the property of Publicidad Permanyer SLU and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

PURPOSE Chest pain frequently poses a diagnostic challenge for general practitioners (GPs). Utilizing risk stratification tools might help GPs to rule out acute coronary syndrome (ACS) and make appropriate referral decisions. We conducted a systematic review of studies evaluating risk stratification tools for chest pain in primary care settings, both with and without troponin assays. Our aims were to assess the performance of tools for ruling out ACS and to provide a comprehensive review of the current evidence. METHODS We searched PubMed and Embase for articles up to October 9, 2023 concerning adult patients with acute chest pain in primary care settings, for whom risk stratification tools (clinical decision rules [CDRs] and/or single biomarker tests) were used. To identify eligible studies, a combination of active learning and backward snowballing was applied. Screening, data extraction, and quality assessment (following the Quality Assessment of Diagnostic Accuracy Studies-2 tool) were performed independently by 2 researchers. RESULTS Of the 1,204 studies screened, 14 were included in the final review. Nine studies validated 7 different CDRs without troponin. Sensitivities ranged from 75.0% to 97.0%, and negative predictive values (NPV) ranged from 82.4% to 99.7%. None of the CDRs outperformed the unaided judgment of GP’s. Five studies reported on strategies using troponin measurements. Studies using high-sensitivity troponin showed highest diagnostic accuracy with sensitivity 83.3% to 100% and NPV 98.8% to 100%. CONCLUSION Clinical decision rules without troponin and the use of conventional troponin showed insufficient sensitivity to rule out ACS in primary care and are not recommended as standalone tools. High-sensitivity troponin strategies are promising, but studies are limited. Further prospective validation in primary care is needed before implementation. [ABSTRACT FROM AUTHOR]

Introduction: The Extracorporeal Treatments in Poisoning (EXTRIP) workgroup suggests hemodialysis in severe lithium poisoning if specific criteria are met. One criterion is if the expected time to obtain a lithium concentration <1.0 mEq/L with optimal management is >36 h. There are a lack of data regarding which patient characteristics are associated with the rate at which patients achieve a lithium concentration <1.0 mEq/L. Methods: We conducted a retrospective chart review analyzing hospital electronic medical records. Inclusion criteria consisted of a lithium concentration >1.2 mEq/L during hospitalization. We excluded patients who received extracorporeal treatment before 36 h elapsed from time of initial lithium concentration >1.2 mEq/L. The primary analysis consisted of a Cox regression and a secondary analysis evaluated the nomogram method described by Buckley and colleagues for predicting prolonged supratherapeutic lithium concentration. Results: One hundred and one patients were included in the study. The median time to reach a lithium concentration <1.0 mEq/L was 42.5 h (IQR: 33.8–51.1). Older patients, patients taking a thiazide, angiotensin-converting enzyme inhibitor or angiotensin receptor blocker, patients with a higher initial lithium concentration, and patients with higher sodium concentrations achieved a lithium concentration <1 mEq/L at a slower rate. For the nomogram analysis, sensitivity (61.5%) and specificity (54.5%) were moderate, the positive predictive value (16.7%) was poor, and the negative predictive value (90.6%) was excellent. Discussion: The results from our primary analysis suggest that identifying higher serum sodium concentration and use of certain antihypertensives that decrease glomerular filtration rate as predictors of an increased time to reach a therapeutic lithium concentration may help identify patients who meet the Extracorporeal Treatments in Poisoning criteria for hemodialysis. The nomogram method performed similarly to prior validation studies. Conclusions: In this retrospective chart review of patients with supratherapeutic lithium concentrations, we identified several risk factors for prolonged supratherapeutic lithium concentrations. [ABSTRACT FROM AUTHOR]

Neonates with critical congenital heart disease of the ductal-dependent pulmonary circulation type (CCHD-DDPC) require prostaglandin E1 (PGE1) to maintain oxygen saturation until surgery. However, the factors contributing to the maintenance doses of PGE1 remain unclear. This study aimed to determine the predictors of high maintenance PGE1 doses in these neonates. This retrospective cohort study included neonates with CCHD-DDPC at Songklanagarind Hospital between January 1, 2006, and December 31, 2021. Factors associated with high maintenance PGE1 doses (> 0.01 mcg/kg/min) were analyzed to identify predictors. Odds ratios were calculated using tabulation and logistic regression analysis. A prediction score was developed for high maintenance PGE1 doses. Among 96 neonates with CCHD-DDPC, 55 % required high maintenance doses of PGE1. Three factors significantly associated with high maintenance PGE1 doses were patent ductus arteriosus (PDA) size-to-birthweight ratio ≤1.3 mm/kg, initial PGE1 dose >0.03 mcg/kg/min, and preoperative invasive mechanical ventilation. The area under the receiver operating characteristic curve for these three predictors was 0.7409. A predictive score of 0–3 was created based on these factors. The probabilities of receiving a high maintenance dose of PGE1 for patients with overall scores of 0, 1, 2, and 3 were 0.19 (95 % CI: 0.04–0.33), 0.42 (95 % CI: 0.30–0.54), 0.69 (95 % CI: 0.57–0.81), and 0.87 (95 % CI: 0.76–0.99), respectively. In neonates with CCHD-DDPC, a PDA size-to-birth weight ratio ≤1.3 mm/kg, an initial dose of PGE1 > 0.03 mcg/kg/min, and preoperative invasive mechanical ventilation were predictors of high maintenance PGE1 doses during the preoperative period. [ABSTRACT FROM AUTHOR]

Abstract Objective This study aimed to identify predictors of brain CT abnormalities in patients who sustained a mild traumatic brain injury (mTBI). Methods Retrospective observational cohort of adult patients with mTBI (Glasgow Coma Score 13–15) that occurred within the preceding 24 h. Results 2548 (91%) of the cohort had a brain CT and 698 (27%) demonstrated abnormal findings. The most frequently observed CT abnormalities were bleeding (638, 25%) and fractures (190, 7.4%). Multivariate logistic regression analysis revealed several significant predictors associated with the presence of brain CT abnormalities including older age [P

Abstract The purpose of this study is to develop a nomogram model for early prediction of the severe mycoplasma pneumoniae pneumonia (SMPP) in Pediatric and Adult Patients. A retrospective analysis was conducted on patients with MPP, classifying them into SMPP and non-severe MPP (NSMPP) groups. A total of 550 patients (NSMPP 374 and SMPP 176) were enrolled in the study and allocated to training, validation cohorts. 278 patients (NSMPP 224 and SMPP 54) were retrospectively collected from two institutions and allocated to testing cohort. The risk factors for SMPP were identified using univariate analysis. For radiomic feature selection, Spearman’s correlation and the least absolute shrinkage and selection operator (LASSO) were utilized. Logistic regression was used to build different models, including clinical, imaging, radiomics, and integrated models (combining clinical, imaging, and radiomics features selected). The model’s discrimination was evaluated using a receiver operating characteristic curve, its calibration with a calibration curve, and the results were visualized using the Hosmer–Lemeshow goodness-of-fit test. Thirteen clinical features and fourteen imaging features were selected for constructing the clinical and imaging models. Simultaneously, a set of twenty-five radiomics features were utilized to build the radiomics model. The integrated model demonstrated good calibration and discrimination in the training cohorts (AUC, 0.922; 95% CI: 0.900, 0.942), validation cohorts (AUC, 0.879; 95% CI: 0.806, 0.920), and testing cohorts (AUC, 0.877; 95% CI: 0.836, 0.916). The discriminatory and predictive efficacy of the clinical model in testing cohorts increased further after clinical and radiological features were incorporated (AUC, 0.849 vs. 0.922, P = 0.002). The model demonstrated exemplary predictive efficacy for SMPP by leveraging a comprehensive set of inputs, encompassing clinical data, quantitative and qualitative radiological features, along with radiomics features. The integration of these three aspects in the predictive model further enhanced the performance of the clinical model, indicating the potential for extensive clinical applications.

Background: Neonates with critical congenital heart disease of the ductal-dependent pulmonary circulation type (CCHD-DDPC) require prostaglandin E1 (PGE1) to maintain oxygen saturation until surgery. However, the factors contributing to the maintenance doses of PGE1 remain unclear. This study aimed to determine the predictors of high maintenance PGE1 doses in these neonates. Methods: This retrospective cohort study included neonates with CCHD-DDPC at Songklanagarind Hospital between January 1, 2006, and December 31, 2021. Factors associated with high maintenance PGE1 doses (> 0.01 mcg/kg/min) were analyzed to identify predictors. Odds ratios were calculated using tabulation and logistic regression analysis. A prediction score was developed for high maintenance PGE1 doses. Results: Among 96 neonates with CCHD-DDPC, 55 % required high maintenance doses of PGE1. Three factors significantly associated with high maintenance PGE1 doses were patent ductus arteriosus (PDA) size-to-birthweight ratio ≤1.3 mm/kg, initial PGE1 dose >0.03 mcg/kg/min, and preoperative invasive mechanical ventilation. The area under the receiver operating characteristic curve for these three predictors was 0.7409. A predictive score of 0–3 was created based on these factors. The probabilities of receiving a high maintenance dose of PGE1 for patients with overall scores of 0, 1, 2, and 3 were 0.19 (95 % CI: 0.04–0.33), 0.42 (95 % CI: 0.30–0.54), 0.69 (95 % CI: 0.57–0.81), and 0.87 (95 % CI: 0.76–0.99), respectively. Conclusions: In neonates with CCHD-DDPC, a PDA size-to-birth weight ratio ≤1.3 mm/kg, an initial dose of PGE1 > 0.03 mcg/kg/min, and preoperative invasive mechanical ventilation were predictors of high maintenance PGE1 doses during the preoperative period.

Abstract Background Although patients with shoulder complaints are frequently referred to physiotherapy, putative predictive factors for outcomes are still unclear. In this regard, only a limited amount of scientific data for patients with subacromial pain syndrome exist, with inconsistent results. An improved knowledge about the ability of baseline variables to predict outcomes could help patients make informed treatment decisions, prevent them from receiving ineffective treatments, and minimize the risk of developing chronic pain. Aim The aims of this secondary longitudinal analysis are threefold: First, to investigate baseline differences between patients with and without successful long-term outcomes following physiotherapy. Second, to compare the predictive ability of two sets of putative predictive variables on outcomes, one based on the literature and one based on the data of the original trial. Third, to explore the contribution of short-term follow-up data to predictive models. Methods Differences between responders and nonresponders were calculated. The predictive ability of variables defined through literature and of variables based on the Akaike Information Criterion (AIC) from the original trial dataset on the Shoulder Pain and Disability Index and the Patients’ Global Impression of Change at the one-year follow-up were analyzed. To test the robustness of the results, different statistical models were used. To investigate the contribution of follow-up data to prediction, short-term data were included in the analyses. Results A sample of 87 patients with subacromial pain syndrome was analyzed. 77% (n = 67) of these participants were classified as responders. Higher expectations and short-term change scores were positive, and higher fear avoidance beliefs, greater baseline disability and pain levels were negative predictors of long-term outcomes in patients with subacromial pain syndrome. Conclusions Although our results are in line with previous research and support the use of clinical factors for prediction, our findings suggest that psychological factors, especially patient expectations and fear avoidance beliefs, also contribute to long-term outcomes and should therefore be considered in the clinical context and further research. However, the hypotheses and recommendations generated from our results need to be confirmed in further studies due to their explorative nature. Trial registration The original trial was registered at Current Controlled Trials under the trial registration number ISRCTN86900354 on March 17, 2010.

Background/purpose: Predictive modeling aids in identifying patients at high risk of adverse events. Using routinely collected data, we report a competing risk prediction model for kidney failure. Methods: A total of 5138 patients with CKD stages 3b–5 were included and randomized into the development and validation cohorts at a ratio of 7:3. The outcome was end-stage kidney disease, defined as the initiation of dialysis or kidney transplantation. All patients were followed-up until December 31, 2020. A Fine and Gray model was applied to estimate the sub-hazard ratio of kidney failure, with death as a competing event. Results: In the development cohort, the mean age was 67.6 ± 13.9 years and 60 % were male. The mean index eGFR and median urinary protein-creatinine ratio (UPCR) were 26.5 ± 12.8 mL/min/1.73 m2 and 1051 mg/g, respectively. The median follow-up duration was 1051 days. The proportion of patients with kidney failure and death was 25.4 % and 14.1 %, respectively. Four models were applied, including eGFR, age, sex, UPCR, systolic and diastolic blood pressure, serum albumin, phosphate, uric acid, haemoglobin, and potassium levels had the best goodness of fit. All models had good discrimination with time-to-event c statistics of 0.89–0.95 in the development cohort and 0.86–0.95 in the validation cohort. The prediction models showed excellent and fairly good calibration at 2 and 5-year risk, respectively. Conclusion: Using real-world data, our competing risk model can accurately predict progression to kidney failure over 2 years in patients with advanced CKD.

The optimal diagnosis strategy for pulmonary embolism (PE) in the emergency department (ED) remains complex. This review summarizes PE diagnosis with clinical presentation, decision rules and investigations for acute PE. This review was performed using studies published between January 1, 2010, and September 1, 2023. PE should be considered in ED in patients with chest pain, shortness of breath, syncope or signs of deep veinous thrombosis. Definitive diagnosis of PE relies on thoracic imaging, with the use of chest tomographic pulmonary angiogram or ventilation-perfusion lung scintigraphy. To limit the continuous increased use of chest imaging, the clinical probability should be the first step for PE workup. The pulmonary embolism rule-out criteria (PERC) can rule out PE at this stage. If not, for low or intermediate probability, several clinical decision rules have been validated, either by ruling out PE on clinical signs, or by raising D-dimer thresholds (YEARS or PEGeD [Pulmonary Embolism Graduated D-Dimer] criteria) or by combination of these different rules. It is recommended that patients with a high clinical probability of PE should undergo chest imaging without the need for D-dimer testing. The PE diagnostic approach can be tailored in specific populations such as pregnant, younger, COVID-19, or cancer patients. PE diagnosis worup illustrates the complexity of modern probabilistic-based approaches of decision-making in medicine. It is recommended to use a Bayesian approach with the evaluation of clinical probability, then order D-dimer if the PERC rule is positive, then adapt the D-dimer threshold for ordering chest imaging using clinical decision rules.

Abstract The decision to intubate a patient with acute hypoxemic respiratory failure who is not in apparent respiratory distress is one of the most difficult clinical decisions faced by intensivists. A conservative approach exposes patients to the dangers of hypoxemia, while a liberal approach exposes them to the dangers of inserting an endotracheal tube and invasive mechanical ventilation. To assist intensivists in this decision, investigators have used various thresholds of peripheral or arterial oxygen saturation, partial pressure of oxygen, partial pressure of oxygen-to-fraction of inspired oxygen ratio, and arterial oxygen content. In this review we will discuss how each of these oxygenation indices provides inaccurate information about the volume of oxygen transported in the arterial blood (convective oxygen delivery) or the pressure gradient driving oxygen from the capillaries to the cells (diffusive oxygen delivery). The decision to intubate hypoxemic patients is further complicated by our nescience of the critical point below which global and cerebral oxygen supply become delivery-dependent in the individual patient. Accordingly, intubation requires a nuanced understanding of oxygenation indexes. In this review, we will also discuss our approach to intubation based on clinical observations and physiologic principles. Specifically, we consider intubation when hypoxemic patients, who are neither in apparent respiratory distress nor in shock, become cognitively impaired suggesting emergent cerebral hypoxia. When deciding to intubate, we also consider additional factors including estimates of cardiac function, peripheral perfusion, arterial oxygen content and its determinants. It is not possible, however, to pick an oxygenation breakpoint below which the benefits of mechanical ventilation decidedly outweigh its hazards. It is futile to imagine that decision making about instituting mechanical ventilation in an individual patient can be condensed into an algorithm with absolute numbers at each nodal point. In sum, an algorithm cannot replace the presence of a physician well skilled in the art of clinical evaluation who has a deep understanding of pathophysiologic principles.

Aim: To develop and internally validate risk prediction models for subsequent clinical deterioration, unplanned ICU admission and death among ward patients following medical emergency team (MET) review. Design: A retrospective cohort study of 1500 patients who remained on a general ward following MET review at an Australian quaternary hospital. Method: Logistic regression was used to model (1) subsequent MET review within 48 h, (2) unplanned ICU admission within 48 h and (3) hospital mortality. Models included demographic, clinical and illness severity variables. Model performance was evaluated using discrimination and calibration with optimism‐corrected bootstrapped estimates. Findings are reported using the TRIPOD guideline for multivariable prediction models for prognosis or diagnosis. There was no patient or public involvement in the development and conduct of this study. Results: Within 48 h of index MET review, 8.3% (n = 125) of patients had a subsequent MET review, 7.2% (n = 108) had an unplanned ICU admission and in‐hospital mortality was 16% (n = 240). From clinically preselected predictors, models retained age, sex, comorbidity, resuscitation limitation, acuity‐dependency profile, MET activation triggers and whether the patient was within 24 h of hospital admission, ICU discharge or surgery. Models for subsequent MET review, unplanned ICU admission, and death had adequate accuracy in development and bootstrapped validation samples. Conclusion: Patients requiring MET review demonstrate complex clinical characteristics and the majority remain on the ward after review for deterioration. A risk score could be used to identify patients at risk of poor outcomes after MET review and support general ward clinical decision‐making. Relevance to clinical practice: Our risk calculator estimates risk for patient outcomes following MET review using clinical data available at the bedside. Future validation and implementation could support evidence‐informed team communication and patient placement decisions. [ABSTRACT FROM AUTHOR]

Introduction: An assortment of alerts has been employed to influence provider order entry, yet many medication orders still require dose adjustment by pharmacists upon order verification. Objectives: The primary goals of this study were to evaluate the impact of adding rule‐based decision support to the computerized provider order entry system on the need for medication dose adjustment by a pharmacist and the occurrence of acute kidney injury (AKI) among patients. Methods: This was a retrospective, pre‐ and post‐implementation observational study on the integration of rule‐based logic into the computerized provider order entry system to automatically select default doses and frequencies for weight‐based or renally‐cleared medications in alignment with health system guidelines. The primary end points were the proportion of medication orders that required pharmacist intervention for dose adjustment and the number of times the AKI pop‐up alert was triggered. Results: After inclusion and exclusion criteria were applied to all available orders, there were 47 393 and 45 767 orders included for final analysis in the pre‐ and post‐implementation periods, respectively. The post‐implementation period showed a significant reduction in pharmacist dosing interventions, with a relative risk of 0.42 (95% confidence interval [CI]: 0.40–0.43; p < 0.0001) and a reduction in AKI (relative risk = 0.58 [95% CI: 0.53–0.64; p < 0.0001]). Conclusion: This study demonstrates the potential of rule‐based decision support to improve initial medication dose selection, reduce the occurrence of AKI, and reduce pharmacist workload, all without increasing alert fatigue. [ABSTRACT FROM AUTHOR]

Background: Although patients with shoulder complaints are frequently referred to physiotherapy, putative predictive factors for outcomes are still unclear. In this regard, only a limited amount of scientific data for patients with subacromial pain syndrome exist, with inconsistent results. An improved knowledge about the ability of baseline variables to predict outcomes could help patients make informed treatment decisions, prevent them from receiving ineffective treatments, and minimize the risk of developing chronic pain. Aim: The aims of this secondary longitudinal analysis are threefold: First, to investigate baseline differences between patients with and without successful long-term outcomes following physiotherapy. Second, to compare the predictive ability of two sets of putative predictive variables on outcomes, one based on the literature and one based on the data of the original trial. Third, to explore the contribution of short-term follow-up data to predictive models. Methods: Differences between responders and nonresponders were calculated. The predictive ability of variables defined through literature and of variables based on the Akaike Information Criterion (AIC) from the original trial dataset on the Shoulder Pain and Disability Index and the Patients' Global Impression of Change at the one-year follow-up were analyzed. To test the robustness of the results, different statistical models were used. To investigate the contribution of follow-up data to prediction, short-term data were included in the analyses. Results: A sample of 87 patients with subacromial pain syndrome was analyzed. 77% (n = 67) of these participants were classified as responders. Higher expectations and short-term change scores were positive, and higher fear avoidance beliefs, greater baseline disability and pain levels were negative predictors of long-term outcomes in patients with subacromial pain syndrome. Conclusions: Although our results are in line with previous research and support the use of clinical factors for prediction, our findings suggest that psychological factors, especially patient expectations and fear avoidance beliefs, also contribute to long-term outcomes and should therefore be considered in the clinical context and further research. However, the hypotheses and recommendations generated from our results need to be confirmed in further studies due to their explorative nature. Trial registration: The original trial was registered at Current Controlled Trials under the trial registration number ISRCTN86900354 on March 17, 2010. [ABSTRACT FROM AUTHOR]

Predictive modeling aids in identifying patients at high risk of adverse events. Using routinely collected data, we report a competing risk prediction model for kidney failure. A total of 5138 patients with CKD stages 3b–5 were included and randomized into the development and validation cohorts at a ratio of 7:3. The outcome was end-stage kidney disease, defined as the initiation of dialysis or kidney transplantation. All patients were followed-up until December 31, 2020. A Fine and Gray model was applied to estimate the sub-hazard ratio of kidney failure, with death as a competing event. In the development cohort, the mean age was 67.6 ± 13.9 years and 60 % were male. The mean index eGFR and median urinary protein-creatinine ratio (UPCR) were 26.5 ± 12.8 mL/min/1.73 m2 and 1051 mg/g, respectively. The median follow-up duration was 1051 days. The proportion of patients with kidney failure and death was 25.4 % and 14.1 %, respectively. Four models were applied, including eGFR, age, sex, UPCR, systolic and diastolic blood pressure, serum albumin, phosphate, uric acid, haemoglobin, and potassium levels had the best goodness of fit. All models had good discrimination with time-to-event c statistics of 0.89–0.95 in the development cohort and 0.86–0.95 in the validation cohort. The prediction models showed excellent and fairly good calibration at 2 and 5-year risk, respectively. Using real-world data, our competing risk model can accurately predict progression to kidney failure over 2 years in patients with advanced CKD. [ABSTRACT FROM AUTHOR]

Copyright of Annales Françaises de Médecine d'Urgence is the property of John Libbey Eurotext Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Anaesthesiologie & Intensivmedizin is the property of DGAI e.V. - Deutsche Gesellschaft fur Anasthesiologie und Intensivmedizin e.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The decision to intubate a patient with acute hypoxemic respiratory failure who is not in apparent respiratory distress is one of the most difficult clinical decisions faced by intensivists. A conservative approach exposes patients to the dangers of hypoxemia, while a liberal approach exposes them to the dangers of inserting an endotracheal tube and invasive mechanical ventilation. To assist intensivists in this decision, investigators have used various thresholds of peripheral or arterial oxygen saturation, partial pressure of oxygen, partial pressure of oxygen-to-fraction of inspired oxygen ratio, and arterial oxygen content. In this review we will discuss how each of these oxygenation indices provides inaccurate information about the volume of oxygen transported in the arterial blood (convective oxygen delivery) or the pressure gradient driving oxygen from the capillaries to the cells (diffusive oxygen delivery). The decision to intubate hypoxemic patients is further complicated by our nescience of the critical point below which global and cerebral oxygen supply become delivery-dependent in the individual patient. Accordingly, intubation requires a nuanced understanding of oxygenation indexes. In this review, we will also discuss our approach to intubation based on clinical observations and physiologic principles. Specifically, we consider intubation when hypoxemic patients, who are neither in apparent respiratory distress nor in shock, become cognitively impaired suggesting emergent cerebral hypoxia. When deciding to intubate, we also consider additional factors including estimates of cardiac function, peripheral perfusion, arterial oxygen content and its determinants. It is not possible, however, to pick an oxygenation breakpoint below which the benefits of mechanical ventilation decidedly outweigh its hazards. It is futile to imagine that decision making about instituting mechanical ventilation in an individual patient can be condensed into an algorithm with absolute numbers at each nodal point. In sum, an algorithm cannot replace the presence of a physician well skilled in the art of clinical evaluation who has a deep understanding of pathophysiologic principles. [ABSTRACT FROM AUTHOR]

In Acute Admission Wards, vital signs are commonly measured only intermittently. This may result in failure to detect early signs of patient deterioration and impede timely identification of patient stability, ultimately leading to prolonged stays and avoidable hospital admissions. Therefore, continuous vital sign monitoring may improve hospital efficacy. The objective of this randomized controlled trial was to evaluate the effect of continuous monitoring on the proportion of patients safely discharged home directly from an Acute Admission Ward. Patients were randomized to either the control group, which received usual care, or the sensor group, which additionally received continuous monitoring using a wearable sensor. The continuous measurements could be considered in discharge decision-making by physicians during the daily bedside rounds. Safe discharge was defined as no unplanned readmissions, emergency department revisits or deaths, within 30 days after discharge. Additionally, length of stay, the number of Intensive Care Unit admissions and Rapid Response Team calls were assessed. In total, 400 patients were randomized, of which 394 completed follow-up, with 196 assigned to the sensor group and 198 to the control group. The proportion of patients safely discharged home was 33.2% in the sensor group and 30.8% in the control group (p = 0.62). No significant differences were observed in secondary outcomes. The trial was terminated prematurely due to futility. In conclusion, continuous monitoring did not have an effect on the proportion of patients safely discharged from an Acute Admission Ward. Implementation challenges of continuous monitoring may have contributed to the lack of effect observed. Trial registration: https://clinicaltrials.gov/ct2/show/NCT05181111. Registered: January 6, 2022. [ABSTRACT FROM AUTHOR]

Background: Long-term outcome after a first venous thromboembolism (VTE) might be optimized by tailoring anticoagulant treatment duration on individual risks of recurrence and major bleeding. The L-TRRiP models (A–D) were previously developed in data from the Dutch Multiple Environment and Genetic Assessment of Risk Factors for Venous thrombosis study to predict VTE recurrence. Objectives: We aimed to externally validate models C and D using data from the United States Heart and Vascular Health (HVH) study. Methods: Data from participants with a first VTE who discontinued initial anticoagulant therapy were used to determine model performance. Missing data were imputed, and results were pooled according to Rubin’s rules. To determine discrimination, Harrell’s C-statistic was calculated. To assess calibration, the observed/expected (O/E) ratio was estimated, and calibration plots were created, in which we accounted for the competing risk of death. A stratified analysis based on age 70 years was performed. Results: Of 1430 participants from the HVH study, 187 experienced an unprovoked VTE recurrence during follow-up. The C-statistics of L-TRRIP models C and D were 0.62 (95% CI, 0.56-0.67) and 0.61 (95% CI, 0.55-0.67), respectively. The O/E ratio (1.00; 95% CI, 0.84-1.17 and 1.09; 95% CI, 0.91-1.27, respectively) and calibration plots indicated good calibration. The discrimination was similar between participants 70 years, whereas overall calibration was lower in participants

BackgroundHigh loop gain (unstable ventilatory control) is an important-but difficult to measure-contributor to obstructive sleep apnea (OSA) pathogenesis, predicting OSA sequelae and/or treatment response. Our objective was to develop and validate a clinical prediction tool of loop gain.MethodsA retrospective cohort of consecutive adults with OSA (apnea-hypopnea index, AHI > 5/hour) based on in-laboratory polysomnography 01/2017-12/2018 was randomly split into a training and test-set (3:1-ratio). Using a customized algorithm ("reference standard") loop gain was quantified from raw polysomnography signals on a continuous scale and additionally dichotomized (high > 0.7). Candidate predictors included general patient characteristics and routine polysomnography data. The model was developed (training-set) using linear regression with backward selection (tenfold cross-validated mean square errors); the predicted loop gain of the final linear regression model was used to predict loop gain class. More complex, alternative models including lasso regression or random forests were considered but did not meet pre-specified superiority-criteria. Final model performance was validated on the test-set.ResultsThe total cohort included 1055 patients (33% high loop gain). Based on the final model, higher AHI (beta = 0.0016; P

Background: Vitamin D deficiency is strongly associated with the development of several diseases. In the current context of a global pandemic of vitamin D deficiency, it is critical to identify people at high risk of vitamin D deficiency. There are no prediction tools for predicting the risk of vitamin D deficiency in the general community population, and this study aims to use machine learning to predict the risk of vitamin D deficiency using data that can be obtained through simple interviews in the community. Methods: The National Health and Nutrition Examination Survey 2001-2018 dataset is used for the analysis which is randomly divided into training and validation sets in the ratio of 70:30. GBM, LR, NNet, RF, SVM, XGBoost methods are used to construct the models and their performance is evaluated. The best performed model was interpreted using the SHAP value and further development of the online web calculator. Results: There were 62,919 participants enrolled in the study, and all participants included in the study were 2 years old and above, of which 20,204 (32.1%) participants had vitamin D deficiency. The models constructed by each method were evaluated using AUC as the primary evaluation statistic and ACC, PPV, NPV, SEN, SPE, F1 score, MCC, Kappa, and Brier score as secondary evaluation statistics. Finally, the XGBoost-based model has the best and near-perfect performance. The summary plot of SHAP values shows that the top three important features for this model are race, age, and BMI. An online web calculator based on this model can easily and quickly predict the risk of vitamin D deficiency. Conclusion: In this study, the XGBoost-based prediction tool performs flawlessly and is highly accurate in predicting the risk of vitamin D deficiency in community populations. [ABSTRACT FROM AUTHOR]

The study addresses the detection of anxiety symptoms in young people using artificial intelligence models. Questionnaires such as the Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder 7-item scale (GAD-7) are used to collect data, with a focus on early detection of anxiety. Three machine learning models are employed: Support Vector Machine (SVM), K Nearest Neighbors (KNN), and Random Forest (RF), with cross-validation to assess their effectiveness. Results show that the RF model is the most efficient, with an accuracy of 91 %, surpassing previous studies. Significant predictors of anxiety are identified, such as parental education level, alcohol consumption, and social security affiliation. A relationship is observed between anxiety and personal and family history of mental illness, as well as with characteristics external to the model, such as family and personal history of depression. The analysis of the results highlights the importance of considering not only clinical but also social and family aspects in mental health interventions. It is suggested that the sample size be expanded in future studies to improve the robustness of the model. In summary, the study demonstrates the usefulness of artificial intelligence in the early detection of anxiety in young people and highlights the relevance of addressing multidimensional factors in the assessment and treatment of this condition.

Introduction: Sepsis is a significant and common cause of death and burden among critically ill patients, which has increasing incidence and mortality in adults over 60 and advanced age. This study aimed to develop an easy-to-use clinical tool for assessing 28-day mortality risk in older sepsis patients upon their initial assessment in the emergency department (ED). Method: A retrospective cohort study was conducted using electronic medical records of older (≥60 years) ED patients with suspected sepsis from August 1, 2018, to December 31, 2018. A new prediction score was formulated based on the logistic coefficients of clinical predictors through multivariable regression analyses. Then, the score's screening performance was evaluated and compared to existing scoring systems; Systemic Inflammatory Response Syndrome (SIRS), quick Sequential Organ Failure Assessment (qSOFA), National early warning score (NEWS), and The Ramathibodi early warning score (REWS); using receiver operating characteristic curve analysis (AuROC). Result: The study included 599 patients with the mean age of 77.13 (range: 60-101) years (56.43% male) and an overall 28-day mortality rate of 7.01%. The newly developed prediction score had seven independent predictors of 28-day mortality: malignancy, dependent status, heart rate, respiratory rate, oxygen saturation, consciousness, and lactate, which demonstrated excellent discriminative ability (AuROC: 0.87, 95% confidence interval (CI): 0.82 - 0.92), significantly outperforming SIRS (AuROC: 0.62), qSOFA (AuROC: 0.72), NEWS (AuROC: 0.74), and REWS (AuROC: 0.71), all with p-values

Background: Acute appendicitis is the most common type of acute abdomen that requires surgical intervention in children. According to general pediatric textbooks, the presence of vomiting before abdominal pain is considered a classic patient history item for excluding acute appendicitis. However, its diagnostic performance in the pediatric population has yet to be investigated. Methods: This was a single-center retrospective observational study involving 134 children who were admitted to the hospital with both abdominal pain and vomiting. The reference standard for appendicitis was defined by computed tomography scanning. The diagnostic performance of “abdominal pain before vomiting” was calculated and compared to those of the Alvarado score and pediatric appendicitis score. Results: The diagnostic performance of “abdominal pain before vomiting” was as follows: sensitivity of 75.3% (95% confidence interval [CI], 64.7–83.6), specificity of 25.0% (95% CI, 15.5–36.7), positive likelihood ratio of 1.00 (95% CI, 0.82–1.22), negative likelihood ratio of 0.99 (95% CI, 0.54–1.79), and diagnostic odds ratio of 1.02 (95% CI, 0.46–2.25). In contrast, the Alvarado score and pediatric appendicitis score (with a threshold of 4 points) demonstrated favorable sensitivity (98.3% [95% CI, 92.4–99.6]), negative predictive value (94.6% [95% CI, 78.4–98.8]), negative likelihood ratio (0.04 [95% CI, 0.01–0.23]), and diagnostic odds ratio (49.9 [95% CI, 6.88–243.2]). Conclusion: In this study, “abdominal pain before vomiting” was associated with poor diagnostic performance for ruling out acute pediatric appendicitis. Thus, the diagnosis of acute appendicitis in the pediatric population should be made based on existing validated scoring systems such as the Alvarado score and pediatric appendicitis score.

Background and aimsPatients with nonalcoholic fatty liver disease (NAFLD) cirrhosis benefit from referral to subspecialty care. While several clinical prediction rules exist to identify advanced fibrosis, the cutoff for excluding cirrhosis due to NAFLD is unclear. This analysis compared clinical prediction rules for excluding biopsy-proven cirrhosis in NAFLD.MethodsAdult patients were enrolled in the NASH Clinical Research Network (US) and the Newcastle Cohort (UK). Clinical and laboratory data were collected at enrolment, and a liver biopsy was taken within 1 year of enrolment. Optimal cutoffs for each score (eg, FIB-4) to exclude cirrhosis were derived from the US cohort, and sensitivity, specificity, positive predictive value, negative predictive value and AUROC were calculated. The cutoffs were evaluated in the UK cohort.Results147/1483 (10%) patients in the US cohort had cirrhosis. All prediction rules had similarly high NPV (0.95-0.97). FIB-4 and NAFLD fibrosis scores were the most accurate in characterising patients as having cirrhosis (AUROC 0.84-0.86). 59/494 (12%) patients in the UK cohort had cirrhosis. Prediction rules had high NPV (0.92-0.96), and FIB-4 and NAFLD fibrosis score the most accurate in the prediction of cirrhosis in the UK cohort (AUROC 0.87-0.89).ConclusionsThis cross-sectional analysis of large, multicentre international datasets shows that current clinical prediction rules perform well in excluding cirrhosis with appropriately chosen cutoffs. These clinical prediction rules can be used in primary care to identify patients, particularly those who are white, female, and

Background and Purpose: Patients with knee osteoarthritis (OA) can benefit from resistance training exercises, range of motion and flexibility maintenance, and low‐load aerobic exercises, as per the relevant clinical guidelines. However, certain patients might be unable to progress to higher physical levels despite such physical therapy programs. This study aimed to evaluate the diagnostic accuracy of a screening tool for determining physical activity levels in individuals with OA undergoing standard physical therapy regularly, using likelihood ratios and predictive values. Method: This prospective observational study included 135 patients undergoing standard physical therapy for OA from six medical facilities. The primary outcome was low physical activity or moderate to high physical activity levels based on 1‐month Self‐Rating Frenchay activities index scores. Backward elimination was used to perform binomial logistic regression analysis after identifying the independent variables in a univariate logistic regression analysis. Among the independent variables adopted in the logistic regression model, receiver operating characteristic analysis using Youden's index was performed for quantitative variables, which were converted to binary values at the cut‐off points. Subsequently, the clinical prediction rule (CPR) was derived. Results: According to the binomial logistic regression analysis, age, knee flexion muscle strength, and visual analog scale (VAS) were risk factors for low physical activity, and the CPR was derived from these variables. The pre‐test probability of the low physical activity group was 37.0% (50 out of 135 participants). For a total CPR score of three points (one point for each item: age ≤69 years, knee flexion muscle strength ≤0.36 Nm/kg, and VAS ≥33 mm), the positive likelihood ratio was 13.60 and the post‐test probability increased to 88.9%. Discussion: The CPR identified patients who might not benefit from the standard physical therapy program. This screening tool could improve patient management, allowing for more tailored approaches in physical therapy programs. [ABSTRACT FROM AUTHOR]

Objectives: The role of lymphadenectomy in the management of early endometrial cancer remains controversial. The aim of our study was to evaluate risk factors associated with nodal metastases in endometrial cancer and to devise a predictive risk model based on the significant risk factors in predicting node metastasis. Materials and Methods: A retrospective study was conducted on women diagnosed with uterus-confined endometrial cancer, and who underwent surgical staging with pelvic and/or paraaortic lymphadenectomy from our center during July 1, 2017 to June 30, 2019. Clinical details, Magnetic Resonance imaging (MRI)-detected myometrial invasion, and pre and post-operative histological details of tumor were correlated with pelvic and/or para-aortic lymph node metastasis. Risk factors were assessed using logistic regression model and risk models devised. Results: 128 patients were included in the study. Paraaortic lymphadenectomy was done in 72.7% patients. Nodal metastasis was seen in 14.8% of patients. Logistic regression analyses revealed lymphovascular invasion (P = 0.002), parametrial involvement (P = 0.017) and nonendometrioid histology (P = 0.004) to be significant risk factors. Tumor size >2 cm, grade 3 and deep myometrial invasion had higher risk for nodal metastasis, although non-significant. Risk models were derived with sensitivity of 79-89.5%, specificity of 58.7-69.7%, Negative predictive value (NPV) of 95-97% and accuracy of 63-71%. Conclusion: Lymphovascular invasion, nonendometrioid histology and parametrial involvement are independent predictors of lymph node metastasis in endometrial cancer. Risk models using these risk factors can better predict the risk of nodal metastasis and thus avoid lymph node dissection in low risk patients. Our risk models had reasonably good sensitivity in nodal metastasis prediction and require further validation. [ABSTRACT FROM AUTHOR]

Accurate outcome prediction is desirable post spinal cord injury (SCI), reducing uncertainty for patients and supporting personalized treatments. Numerous attempts have been made to create clinical prediction rules that identify patients who are likely to recover function. It is unknown to what extent these rules are routinely used in clinical practice. To better understand knowledge of, and attitudes toward, clinical prediction rules amongst SCI clinicians in the United Kingdom. An online survey was distributed via mailing lists of clinical special interest groups and relevant National Health Service Trusts. Respondents answered questions about their knowledge of existing clinical prediction rules and their general attitudes to using them. They also provided information about their level of experience with SCI patients. One hundred SCI clinicians completed the survey. The majority (71%) were unaware of clinical prediction rules for SCI; only 8% reported using them in clinical practice. Less experienced clinicians were less likely to be aware. Lack of familiarity with prediction rules was reported as being a barrier to their use. The importance of clinical expertise when making prognostic decisions was emphasized. All respondents reported interest in using clinical prediction rules in the future. The results show widespread lack of awareness of clinical prediction rules amongst SCI clinicians in the United Kingdom. However, clinicians were positive about the potential for clinical prediction rules to support decision-making. More focus should be directed toward refining current rules and improving dissemination within the SCI community. [ABSTRACT FROM AUTHOR]

Acute appendicitis is the most common type of acute abdomen that requires surgical intervention in children. According to general pediatric textbooks, the presence of vomiting before abdominal pain is considered a classic patient history item for excluding acute appendicitis. However, its diagnostic performance in the pediatric population has yet to be investigated. This was a single-center retrospective observational study involving 134 children who were admitted to the hospital with both abdominal pain and vomiting. The reference standard for appendicitis was defined by computed tomography scanning. The diagnostic performance of "abdominal pain before vomiting" was calculated and compared to those of the Alvarado score and pediatric appendicitis score. The diagnostic performance of "abdominal pain before vomiting" was as follows: sensitivity of 75.3% (95% confidence interval [CI], 64.7–83.6), specificity of 25.0% (95% CI, 15.5–36.7), positive likelihood ratio of 1.00 (95% CI, 0.82–1.22), negative likelihood ratio of 0.99 (95% CI, 0.54–1.79), and diagnostic odds ratio of 1.02 (95% CI, 0.46–2.25). In contrast, the Alvarado score and pediatric appendicitis score (with a threshold of 4 points) demonstrated favorable sensitivity (98.3% [95% CI, 92.4–99.6]), negative predictive value (94.6% [95% CI, 78.4–98.8]), negative likelihood ratio (0.04 [95% CI, 0.01–0.23]), and diagnostic odds ratio (49.9 [95% CI, 6.88–243.2]). In this study, "abdominal pain before vomiting" was associated with poor diagnostic performance for ruling out acute pediatric appendicitis. Thus, the diagnosis of acute appendicitis in the pediatric population should be made based on existing validated scoring systems such as the Alvarado score and pediatric appendicitis score. [ABSTRACT FROM AUTHOR]

Objective We examined the influence of 4 different risk information formats on inpatient nurses' preferences and decisions with an acute clinical deterioration decision-support system. Materials and methods We conducted a comparative usability evaluation in which participants provided responses to multiple user interface options in a simulated setting. We collected qualitative data using think aloud methods. We collected quantitative data by asking participants which action they would perform after each time point in 3 different patient scenarios. Results More participants (n  = 6) preferred the probability format over relative risk ratios (n  = 2), absolute differences (n  = 2), and number of persons out of 100 (n  = 0). Participants liked average lines, having a trend graph to supplement the risk estimate, and consistent colors between trend graphs and possible actions. Participants did not like too much text information or the presence of confidence intervals. From a decision-making perspective, use of the probability format was associated with greater concordance in actions taken by participants compared to the other 3 risk information formats. Discussion By focusing on nurses' preferences and decisions with several risk information display formats and collecting both qualitative and quantitative data, we have provided meaningful insights for the design of clinical decision-support systems containing complex quantitative information. Conclusion This study adds to our knowledge of presenting risk information to nurses within clinical decision-support systems. We encourage those developing risk-based systems for inpatient nurses to consider expressing risk in a probability format and include a graph (with average line) to display the patient's recent trends. [ABSTRACT FROM AUTHOR]

Background: Abdominal pain is a frequent adverse event in patients with hepatocellular carcinoma (HCC) after transarterial chemoembolisation (TACE). However, there remains uncertainty regarding the determinants of post-TACE pain. Objectives: We aimed to create and verify a prediction model for postoperative pain in patients with HCC after TACE treatment. Methods: This prospective study included all patients with HCC undergoing TACE in our hospital. According to the time of treatment, the dataset was divided into two cohorts (development and validation) in a3:2 ratio. After TACE, the participants used a visual analog scale to quantify their pain level at rest over a 24-hour period. The age, gender, tumor location, tumor size and number, medication administration route, and presence of portal vein tumor thrombosis (PVTT) were recorded in all patients. Results: In total, 137 (mean age: 60.3 ± 10.1 years; 78.1% male) and 91 (mean age: 61.1 ± 10.5 years; 73.6% male) patients were included in the development and validation cohorts, respectively. Furthermore, 46.0% and 39.6% of the patients experienced acute moderate to severe pain after TACE in the development and validation cohorts, respectively. The tumor location, the drug delivery method, and the presence of PVTT were independently associated with post-TACE pain, all of which were combined to develop a prediction model based on a logistic equation. The discrimination of this risk score was satisfactory in both the development (area under the curve (AUC): 0.693,95% confidence interval (CI): 0.609 to 0.769, P < 0.001) and validation (AUC: 0.652,95% CI: 0.544 to 0.748, P = 0.002) cohorts. There was no significant difference between the two cohorts (difference: 0.042,95% CI: -0.081 to 0.164, P= 0.506). The risk score had good specificity for predicting post-TACE pain in both the development (83.8% (95% CI: 73.4% to 91.3%)) and validation (76.4% (95% CI: 63.0% to 86.8%)) cohorts. Conclusions: The presence of PVTT, the tumor location, and the drug administration method were risk factors for post-TACE dis- comfort. A prediction model based on these risk factors was useful for identifying patients who were vulnerable to post-TACE pain. However, further studies are required to validate these findings and optimize the model's performance. [ABSTRACT FROM AUTHOR]

Objectives: To establish a computed tomography (CT)–based scale to evaluate the resectability of locally advanced thyroid cancer. Methods: This twin-centre retrospective study included 95 locally advanced thyroid cancer patients from the 1st centre as the training cohort and 31 patients from the 2nd centre as the testing cohort, who were categorised into the resectable and unresectable groups. Three radiologists scored the CT scans of each patient by evaluating the extension to the recurrent laryngeal nerve (RLN), trachea, oesophagus, artery, vein, soft tissue, and larynx. A 14-score scale (including all comprised structures) and a 12-score scale (excluding larynx) were developed. Receiver-operating characteristic (ROC) analysis was used to evaluate the performance of the scales. Stratified fivefold cross-validation and external verification were used to validate the scale. Results: In the training cohort, compromised RLN (p < 0.001), trachea (p = 0.001), oesophagus (p = 0.002), artery (p < 0.001), vein (p = 0.005), and soft tissue (p < 0.001) were predictors for unresectability, while compromised larynx (p = 0.283) was not. The 12-score scale (AUC = 0.882, 95%CI: 0.812–0.952) was not inferior to the 14-score scale (AUC = 0.891, 95%CI: 0.823–0.960). In subgroup analysis, the AUCs of the 12-score scale were 0.826 for treatment-naïve patients and 0.976 for patients with prior surgery. The 12-score scale was further validated with a fivefold cross-validation analysis, with an overall accuracy of 78.9–89.4%. Finally, external validation using the testing cohort showed an AUC of 0.875. Conclusions: The researchers built a CT-based 12-score scale to evaluate the resectability of locally advanced thyroid cancer. Validation with a larger sample size is required to confirm the efficacy of the scale. Clinical relevance statement: This 12-score CT scale would help clinicians evaluate the resectability of locally advanced thyroid cancer. Key Points: • The researchers built a 12-score CT scale (including recurrent laryngeal nerve, trachea, oesophagus, artery, vein, and soft tissue) to evaluate the resectability of locally advanced thyroid cancer. • This scale has the potential to help clinicians make treatment plans for locally advanced thyroid cancer. [ABSTRACT FROM AUTHOR]

Objectives: COVID-19 pandemic seems to be under control. However, despite the vaccines, 5 to 10% of the patients with mild disease develop moderate to critical forms with potential lethal evolution. In addition to assess lung infection spread, chest CT helps to detect complications. Developing a prediction model to identify at-risk patients of worsening from mild COVID-19 combining simple clinical and biological parameters with qualitative or quantitative data using CT would be relevant to organizing optimal patient management. Methods: Four French hospitals were used for model training and internal validation. External validation was conducted in two independent hospitals. We used easy-to-obtain clinical (age, gender, smoking, symptoms' onset, cardiovascular comorbidities, diabetes, chronic respiratory diseases, immunosuppression) and biological parameters (lymphocytes, CRP) with qualitative or quantitative data (including radiomics) from the initial CT in mild COVID-19 patients. Results: Qualitative CT scan with clinical and biological parameters can predict which patients with an initial mild presentation would develop a moderate to critical form of COVID-19, with a c-index of 0.70 (95% CI 0.63; 0.77). CT scan quantification improved the performance of the prediction up to 0.73 (95% CI 0.67; 0.79) and radiomics up to 0.77 (95% CI 0.71; 0.83). Results were similar in both validation cohorts, considering CT scans with or without injection. Conclusion: Adding CT scan quantification or radiomics to simple clinical and biological parameters can better predict which patients with an initial mild COVID-19 would worsen than qualitative analyses alone. This tool could help to the fair use of healthcare resources and to screen patients for potential new drugs to prevent a pejorative evolution of COVID-19. Clinical Trial Registration: NCT04481620. Clinical relevance statement: CT scan quantification or radiomics analysis is superior to qualitative analysis, when used with simple clinical and biological parameters, to determine which patients with an initial mild presentation of COVID-19 would worsen to a moderate to critical form. Key Points: • Qualitative CT scan analyses with simple clinical and biological parameters can predict which patients with an initial mild COVID-19 and respiratory symptoms would worsen with a c-index of 0.70. • Adding CT scan quantification improves the performance of the clinical prediction model to an AUC of 0.73. • Radiomics analyses slightly improve the performance of the model to a c-index of 0.77. [ABSTRACT FROM AUTHOR]

Introduction: Large vessel occlusion (LVO) strokes are associated with worse functional outcomes and higher mortality rates. In the present systematic review and meta-analysis, we evaluated the diagnostic yield of the Cincinnati Prehospital Stroke Scale (CPSS) in detecting LVO. Methods: We performed an extensive systematic search among online databases including Medline, Embase, Web of Science, and Scopus, until July 31st, 2023. We also conducted a manual search on Google and Google scholar, along with citation tracking to supplement the systematic search in retrieving all studies that evaluated the diagnostic accuracy of the CPSS in detecting LVO among patients suspected to stroke. Results: Fourteen studies were included in the present meta-analysis. CPSS showed the sensitivity of 97% (95% CI: 87%–99%) and the specificity of 17% (95% CI: 4%–54%) at the cut-off point of ≥1. The optimal threshold was determined to be ≥2, with a sensitivity of 82% (95% CI: 74%–88%) and specificity of 62% (95% CI: 48%–74%) in detecting LVO. At the highest cut-off point of ≥3, the CPSS had the lowest sensitivity of 60% (95% CI: 51%–69%) and the highest specificity of 81% (95% CI: 71%–88%). Sensitivity analyses showed the robustness of the results regardless of study population, inclusion of hemorrhagic stroke patients, pre-hospital or in-hospital settings, and the definition of LVO. Conclusion: A very low level of evidence demonstrated that CPSS, with a threshold set at ≥2, is a useful tool for identifying LVO stroke and directing patients to CSCs, both in prehospital and in-hospital settings.

ObjectiveTo create and validate a simple and transferable machine learning model from electronic health record data to accurately predict clinical deterioration in patients with covid-19 across institutions, through use of a novel paradigm for model development and code sharing.DesignRetrospective cohort study.SettingOne US hospital during 2015-21 was used for model training and internal validation. External validation was conducted on patients admitted to hospital with covid-19 at 12 other US medical centers during 2020-21.Participants33 119 adults (≥18 years) admitted to hospital with respiratory distress or covid-19.Main outcome measuresAn ensemble of linear models was trained on the development cohort to predict a composite outcome of clinical deterioration within the first five days of hospital admission, defined as in-hospital mortality or any of three treatments indicating severe illness: mechanical ventilation, heated high flow nasal cannula, or intravenous vasopressors. The model was based on nine clinical and personal characteristic variables selected from 2686 variables available in the electronic health record. Internal and external validation performance was measured using the area under the receiver operating characteristic curve (AUROC) and the expected calibration error-the difference between predicted risk and actual risk. Potential bed day savings were estimated by calculating how many bed days hospitals could save per patient if low risk patients identified by the model were discharged early.Results9291 covid-19 related hospital admissions at 13 medical centers were used for model validation, of which 1510 (16.3%) were related to the primary outcome. When the model was applied to the internal validation cohort, it achieved an AUROC of 0.80 (95% confidence interval 0.77 to 0.84) and an expected calibration error of 0.01 (95% confidence interval 0.00 to 0.02). Performance was consistent when validated in the 12 external medical centers (AUROC range 0.77-0.84), across subgroups of sex, age, race, and ethnicity (AUROC range 0.78-0.84), and across quarters (AUROC range 0.73-0.83). Using the model to triage low risk patients could potentially save up to 7.8 bed days per patient resulting from early discharge.ConclusionA model to predict clinical deterioration was developed rapidly in response to the covid-19 pandemic at a single hospital, was applied externally without the sharing of data, and performed well across multiple medical centers, patient subgroups, and time periods, showing its potential as a tool for use in optimizing healthcare resources.

ImportanceProstate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT) can detect low-volume, nonlocalized (ie, regional or metastatic) prostate cancer that was occult on conventional imaging. However, the long-term clinical implications of PSMA PET/CT upstaging remain unclear.ObjectivesTo evaluate the prognostic significance of a nomogram that models an individual's risk of nonlocalized upstaging on PSMA PET/CT and to compare its performance with existing risk-stratification tools.Design, setting, and participantsThis cohort study included patients diagnosed with high-risk or very high-risk prostate cancer (ie, prostate-specific antigen [PSA] level >20 ng/mL, Gleason score 8-10, and/or clinical stage T3-T4, without evidence of nodal or metastatic disease by conventional workup) from April 1995 to August 2018. This multinational study was conducted at 15 centers. Data were analyzed from December 2020 to March 2021.ExposuresCurative-intent radical prostatectomy (RP), external beam radiotherapy (EBRT), or EBRT plus brachytherapy (BT), with or without androgen deprivation therapy.Main outcomes and measuresPSMA upstage probability was calculated from a nomogram using the biopsy Gleason score, percentage positive systematic biopsy cores, clinical T category, and PSA level. Biochemical recurrence (BCR), distant metastasis (DM), prostate cancer-specific mortality (PCSM), and overall survival (OS) were analyzed using Fine-Gray and Cox regressions. Model performance was quantified with the concordance (C) index.ResultsOf 5275 patients, the median (IQR) age was 66 (60-72) years; 2883 (55%) were treated with RP, 1669 (32%) with EBRT, and 723 (14%) with EBRT plus BT; median (IQR) PSA level was 10.5 (5.9-23.2) ng/mL; 3987 (76%) had Gleason grade 8 to 10 disease; and 750 (14%) had stage T3 to T4 disease. Median (IQR) follow-up was 5.1 (3.1-7.9) years; 1221 (23%) were followed up for at least 8 years. Overall, 1895 (36%) had BCR, 851 (16%) developed DM, and 242 (5%) died of prostate cancer. PSMA upstage probability was significantly prognostic of all clinical end points, with 8-year C indices of 0.63 (95% CI, 0.61-0.65) for BCR, 0.69 (95% CI, 0.66-0.71) for DM, 0.71 (95% CI, 0.67-0.75) for PCSM, and 0.60 (95% CI, 0.57-0.62) for PCSM (P

BackgroundHospitalized patients with COVID-19 have increased risks of venous (VTE) and arterial thromboembolism (ATE). Active cancer diagnosis and treatment are well-known risk factors; however, a risk assessment model (RAM) for VTE in patients with both cancer and COVID-19 is lacking.ObjectivesTo assess the incidence of and risk factors for thrombosis in hospitalized patients with cancer and COVID-19.MethodsAmong patients with cancer in the COVID-19 and Cancer Consortium registry (CCC19) cohort study, we assessed the incidence of VTE and ATE within 90 days of COVID-19-associated hospitalization. A multivariable logistic regression model specifically for VTE was built using a priori determined clinical risk factors. A simplified RAM was derived and internally validated using bootstrap.ResultsFrom March 17, 2020 to November 30, 2020, 2804 hospitalized patients were analyzed. The incidence of VTE and ATE was 7.6% and 3.9%, respectively. The incidence of VTE, but not ATE, was higher in patients receiving recent anti-cancer therapy. A simplified RAM for VTE was derived and named CoVID-TE (Cancer subtype high to very-high risk by original Khorana score +1, VTE history +2, ICU admission +2, D-dimer elevation +1, recent systemic anti-cancer Therapy +1, and non-Hispanic Ethnicity +1). The RAM stratified patients into two cohorts (low-risk, 0-2 points, n = 1423 vs. high-risk, 3+ points, n = 1034) where VTE occurred in 4.1% low-risk and 11.3% high-risk patients (c statistic 0.67, 95% confidence interval 0.63-0.71). The RAM performed similarly well in subgroups of patients not on anticoagulant prior to admission and moderately ill patients not requiring direct ICU admission.ConclusionsHospitalized patients with cancer and COVID-19 have elevated thrombotic risks. The CoVID-TE RAM for VTE prediction may help real-time data-driven decisions in this vulnerable population.

Introduction: MONTH Difficult Laryngoscopy Score was developed for effectively identifying difficult intubations in the emergency department (ED). This study aimed to evaluate the accuracy of MONTH Score in predicting difficult intubations in ED. Methods: We prospectively collected data on all patients undergoing intubation in the ED of Ramathibodi Hospital, Bangkok, Thailand. The screening performance characteristics of the MONTH score in identifying the difficult intubation in ED were analyzed. All data were analyzed using STATA software version 18.0. Results: 324 intubated patients with the median age of 73 (63-82) years were studied (63.58% male). The proportion of difficult intubations was 19.44%. The sensitivity and specificity of MONTH in predicting difficult intubations were 74.6% (95% CI: 61.6%-85.0%) and 92.8% (95% CI: 89.0%-95.6%), respectively. These measures in subgroup of patients with Intubation Difficulty Scale (IDS) score ≥ 6 were 44.1% (95%CI: 31.2-57.6) and 98.5% (95% CI: 96.2%- 99.6%), respectively. The area under the receiver operation characteristic (ROC) curve of MONTH in predicting difficult intubations was 0.895 (95% CI: 0.856- 0.926). Conclusions: It seems that the MONTH Difficult Laryngoscopy Score could be considered as a tool with high specificity and positive predictive values in identifying cases with difficult intubations in ED.

Purpose: We developed a simple self-checkable screening tool for chronic prostatitis (S-CP) and internally validated it to encourage men (in the general population) with possible chronic prostatitis to consult urologists. Methods: The expert panel proposed the S-CP, which comprises three domains: Area of pain or discomfort (6 components), accompanying Symptom (6 components), and Trigger for symptom flares (4 components). We employed logistic regression to predict chronic prostatitis prevalence with the S-CP. We evaluated the predictive performance using data from a representative national survey of Japanese men aged 20 to 84. We calculated the optimism-adjusted area under the curve using bootstrapping. We assessed sensitivity/specificity, likelihood ratio, and predictive value for each cutoff of the S-CP. Results: Data were collected for 5,010 men—71 (1.4%) had a chronic prostatitis diagnosis. The apparent and adjusted area under the curve for the S-CP was 0.765 [95% confidence interval (CI) 0.702, 0.829] and 0.761 (0.696, 0.819), respectively. When the cutoff was two of the three domains being positive, sensitivity and specificity were 62.0% (95% CI 49.7, 73.2) and 85.4% (95% CI 84.4, 86.4), respectively. The positive/negative likelihood ratios were 4.2 (95% CI 3.5, 5.2) and 0.45 (95% CI 0.33, 0.60), respectively. The positive/negative predictive values were 5.7 (95% CI 4.2, 7.6) and 99.4 (95% CI 99.1, 99.6), respectively. Conclusion: The reasonable predictive performance of the S-CP indicated that patients (in the general population) with chronic prostatitis were screened as a first step. Further research would develop another tool for diagnostic support in actual clinical settings. [ABSTRACT FROM AUTHOR]

As pediatric urologists and nephrologists gradually deepen their understandings of pediatric vesicoureteral reflux (VUR), the overall treatment mode has also undergone a great transformation in China. Clinical outcomes of VUR children vary greatly and are influenced by various factors. Based upon risk stratifications, individualized assessment and managements are mandatory. How to monitor and prevent the occurrence of febrile urinary tract infections, evaluate the risk of renal scarring, prevent end-stage renal disease and balance the advantages and disadvantages between interventions and conservative treatments are currently dilemmas. Based upon literature reviews and personal experiences, this review discussed several key issues in the treatment of pediatric VUR. [ABSTRACT FROM AUTHOR]

Background: Spinal epidural abscess (SEA) is a rare, catastrophic condition for which diagnostic delays are common. Our national group develops evidence‐based guidelines, known as clinical management tools (CMT), to reduce high‐risk misdiagnoses. We study whether implementation of our back pain CMT improved SEA diagnostic timeliness and testing rates in the emergency department (ED). Methods: We conducted a retrospective observational study before and after implementation of a nontraumatic back pain CMT for SEA in a national group. Outcomes included diagnostic timeliness and test utilization. We used regression analysis to compare differences before (January 2016–June 2017) and after (January 2018–December 2019) with 95% confidence intervals (CIs) clustered by facility. We graphed monthly testing rates. Results: In 59 EDs, pre versus post periods included 141,273 (4.8%) versus 192,244 (4.5%) back pain visits and 188 versus 369 SEA visits, respectively. After implementation, SEA visits with prior related visits were unchanged (12.2% vs. 13.3%, difference +1.0%, 95% CI –4.5% to 6.5%). Mean number of days to diagnosis decreased but not significantly (15.2 days vs. 11.9 days, difference −3.3 days, 95% CI −7.1 to 0.6 days). Back pain visits receiving CT (13.7% vs. 21.1%, difference +7.3%, 95% CI 6.1% to 8.6%) and MRI (2.9% vs. 4.4%, difference +1.4%, 95% CI 1.0% to 1.9%) increased. Spine X‐rays decreased (22.6% vs. 20.5%, difference 2.1%, 95% CI –4.3% to 0.1%). Back pain visits receiving erythrocyte sedimentation rate or C‐reactive protein increased (1.9% vs. 3.5%, difference +1.6%, 95% CI 1.3% to 1.9%). Conclusions: Back pain CMT implementation was associated with an increased rate of recommended imaging and laboratory testing in back pain. There was no associated reduction in the proportion of SEA cases with a related prior visit or time to SEA diagnosis. [ABSTRACT FROM AUTHOR]

Background: Effective delirium prevention could benefit from automatic risk stratification of older inpatients using routinely collected clinical data. Aim: Primary aim was to develop and validate a delirium prediction model (DELIKT) suitable for implementation in hospitals. Secondary aim was to select an anticholinergic burden scale as a predictor. Method: We used one cohort for model development and another for validation with electronically available data collected within the first 24 h of admission. Included were patients aged ≥ 65, hospitalised ≥ 48 h with no stay > 24 h in an intensive care unit. Predictors, such as administrative and laboratory variables or an anticholinergic burden scale, were selected using a combination of feature selection filter method and forward/backward selection. The final model was based on logistic regression and the DELIKT was derived from the β-coefficients. We report the following performance measures: area under the curve, sensitivity, specificity and odds ratio. Results: Both cohorts were similar and included over 10,000 patients each (mean age 77.6 ± 7.6 years) with 11% experiencing delirium. The model included nine variables: age, medical department, dementia, hemi-/paraplegia, catheterisation, potassium, creatinine, polypharmacy and the anticholinergic burden measured with the Clinician-rated Anticholinergic Scale (CrAS). The external validation yielded an AUC of 0.795. With a cut-off at 20 points in the DELIKT, we received a sensitivity of 79.7%, specificity of 62.3% and an odds ratio of 5.9 (95% CI 5.2, 6.7). Conclusion: The DELIKT is a potentially automatic tool with predictors from standard care including the CrAS to identify patients at high risk for delirium. [ABSTRACT FROM AUTHOR]

Objective: To validate Vergouwe's prediction model using the Swedish and Norwegian Testicular Cancer Group (SWENOTECA) RETROP database and to define its clinical utility. Materials and methods: Vergouwe's prediction model for benign histopathology in post‐chemotherapy retroperitoneal lymph node dissection (PC‐RPLND) uses the following variables: presence of teratoma in orchiectomy specimen; pre‐chemotherapy level of alpha‐fetoprotein; β‐Human chorionic gonadotropin and lactate dehydrogenase; and lymph node size pre‐ and post‐chemotherapy. Our validation cohort consisted of patients included in RETROP, a prospective population‐based database of patients in Sweden and Norway with metastatic nonseminoma, who underwent PC‐RPLND in the period 2007–2014. Discrimination and calibration analyses were used to validate Vergouwe's prediction model results. Calibration plots were created and a Hosmer–Lemeshow test was calculated. Clinical utility, expressed as opt‐out net benefit (NBopt‐out), was analysed using decision curve analysis. Results: Overall, 284 patients were included in the analysis, of whom 130 (46%) had benign histology after PC‐RPLND. Discrimination analysis showed good reproducibility, with an area under the receiver‐operating characteristic curve (AUC) of 0.82 (95% confidence interval 0.77–0.87) compared to Vergouwe's prediction model (AUC between 0.77 and 0.84). Calibration was acceptable with no recalibration. Using a prediction threshold of 70% for benign histopathology, NBopt‐out was 0.098. Using the model and this threshold, 61 patients would have been spared surgery. However, only 51 of 61 were correctly classified as benign. Conclusions: The model was externally validated with good reproducibility. In a clinical setting, the model may identify patients with a high chance of benign histopathology, thereby sparing patients of surgery. However, meticulous follow‐up is required. [ABSTRACT FROM AUTHOR]

ObjectiveWhether patients with acute stroke and large vessel occlusion (LVO) benefit from prehospital identification and diversion by emergency medical services (EMS) to an endovascular stroke therapy (EST)-capable center is controversial. We sought to estimate the accuracy of field-based identification of potential EST candidates in a hypothetical best-of-all-worlds situation.MethodsIn Kaiser Permanente Northern California, all acute stroke patients arriving at its 21 stroke centers between 7:00 am and midnight from January 2016 to December 2019 were evaluated by teleneurologists on arrival. Initial National Institutes of Health Stroke Scale (NIHSS) score, presence of LVO, and referral for EST were obtained from standardized teleneurology notes. Factors associated with LVO were evaluated using generalized estimating equations accounting for clustering by facility.ResultsAmong 13,377 patients brought in by EMS with potential stroke, 7168 (53.6%) were not candidates for acute stroke interventions. Of the remaining 6089 cases, 2,573 (42.3%) had an NIHSS score >10, the cutoff with a higher association for LVO. Only 703 patients (27.3% with NIHSS score >10) were ultimately diagnosed with LVO and referred for EST. Across all NIHSS scores, only 884 (6.6%) suspected acute stroke patients had LVO and EST referral.ConclusionsEven if field-based tools were as accurate as NIHSS scoring and predictions by stroke neurologists, only about 1 in 4 acute stroke patients diverted to EST-capable centers would benefit by receiving EST. Depending on geography and stroke center performance on door-to-needle time, many systems may be better served by focusing on expediting evaluation, treatment with intravenous thrombolysis, and transfer to EST-capable centers.

BackgroundGuidelines recommend that clinicians use clinical prediction models to estimate future risk to guide decisions. For example, predicted fracture risk is a major factor in the decision to initiate bisphosphonate medications. However, current methods for developing prediction models often lead to models that are accurate but difficult to use in clinical settings.ObjectiveThe objective of this study was to develop and test whether a new metric that explicitly balances model accuracy with clinical usability leads to accurate, easier-to-use prediction models.MethodsWe propose a new metric called the Time-cost Information Criterion (TCIC) that will penalize potential predictor variables that take a long time to obtain in clinical settings. To demonstrate how the TCIC can be used to develop models that are easier-to-use in clinical settings, we use data from the 2000 wave of the Health and Retirement Study (n=6311) to develop and compare time to mortality prediction models using a traditional metric (Bayesian Information Criterion or BIC) and the TCIC.ResultsWe found that the TCIC models utilized predictors that could be obtained more quickly than BIC models while achieving similar discrimination. For example, the TCIC identified a 7-predictor model with a total time-cost of 44 seconds, while the BIC identified a 7-predictor model with a time-cost of 119 seconds. The Harrell C-statistic of the TCIC and BIC 7-predictor models did not differ (0.7065 vs. 0.7088, P=0.11).ConclusionAccounting for the time-costs of potential predictor variables through the use of the TCIC led to the development of an easier-to-use mortality prediction model with similar discrimination.