Development of an early prediction model for postoperative delirium in neurosurgical patients admitted to the ICU after elective craniotomy (E-PREPOD-NS): A secondary analysis of a prospective cohort study

Postoperative delirium (POD) is a significant clinical problem in neurosurgical patients after intracranial surgery. Identification of high-risk patients may optimize perioperative management, but an adequate risk model for use at early phase after operation has not been developed. In the secondary analysis of a prospective cohort study, 800 adult patients admitted to the ICU after elective intracranial surgeries were included. The POD was diagnosed as Confusion Assessment Method for the ICU positive on postoperative day 1 to 3. Multivariate logistic regression analysis was used to develop early prediction model (E-PREPOD-NS) and the final model was validated with 200 bootstrap samples. The incidence of POD in this cohort was19.6%. We identified nine variables independently associated with POD in the final model: advanced age (OR 3.336, CI 1.765–6.305, 1 point), low education level (OR 2.528, 1.446–4.419, 1), smoking history (OR 2.582, 1.611–4.140, 1), diabetes (OR 2.541, 1.201–5.377, 1), supra-tentorial lesions (OR 3.424, 2.021–5.802, 1), anesthesia duration > 360 min (OR 1.686, 1.062–2.674, 0.5), GCS < 9 at ICU admission (OR 6.059, 3.789–9.690, 1.5), metabolic acidosis (OR 13.903, 6.248–30.938, 2.5), and neurosurgical drainage tube (OR 1.924, 1.132–3.269, 0.5). The area under the receiver operator curve (AUROC) of the risk score for prediction of POD was 0.865 (95% CI 0.835–0.895). The AUROC was 0.851 after internal validation (95% CI 0.791–0.912). The model showed good calibration. The E-PREPOD-NS model can predict POD in patients admitted to the ICU after elective intracranial surgery with good accuracy. External validation is needed in the future.     

Statistical inference for decision curve analysis,with applications to cataract diagnosis

Statistical learning methods are widely used in medical literature for the purpose of diagnosis or prediction. Conventional accuracy assessment via sensitivity, specificity, and ROC curves does not fully account for clinical utility of a specific model. Decision curve analysis (DCA) becomes a novel complement as it incorporates a clinical judgment of the relative value of benefits (treating a true positive case) and harms (treating a false positive case) associated with prediction models. The preference of a patient or a policy-maker is formulated statistically as the underlying threshold probability, above which the patient would choose to be treated. Net benefit is then calculated for possible threshold probability, which places benefits and harms on the same scale. We consider the inference problems for DCA in this paper. Interval estimation procedure and inference methodology are provided after we derive the relevant asymptotic properties. Our formulation can accommodate the classification problems with multiple categories. We carry out numerical studies to assess the performance of the proposed methods. An eye disease dataset is analyzed to illustrate our proposals.     

Editorial Perspective: Psychosis risk in adolescence – outcomes,comorbidity, and antipsychotics

Research on Clinical High-Risk for Psychosis (CHR-P) has led to a vigorous change in the field of early detection in psychiatry and is gradually expanding its focus toward early development. The Annual Research Review on psychosis risk in adolescents (Journal of Child Psychology and Psychiatry, 62, 2020 and 657) offers a wide-angle meta-analytical picture of such emerging trends in all areas relevant to CHR-P Research, that is, detection, prognosis, and intervention. This editorial perspective is meant to expand the clinical and conceptual reach of these meta-analytic findings in relation to (a) the influence of age on transition rate and scalability of the early detection model across the child–adolescent vs adult periods; (b) potential latent heterogeneity of the pathogenetic trajectories leading to CHR-P as suggested by comorbid psychopathologies; (c) controversial (or at least problematic) prognostic significance of antipsychotic exposure in CHR-P subjects, especially in children and adolescents.     

Turning duration and steps predict future falls in poststroke hemiplegic individuals: A preliminary cohort study

ABSTRACT

Introduction: Turning was reported as one of the activities that most frequently leads to falling among stroke patients. This study investigated whether the duration and steps of a 180° turn while walking can distinguish retrospective fallers from non-fallers and predict future falls in a 1-year period in patients with poststroke hemiplegia.

Methods: Thirty stroke patients were recruited. They were instructed to get up from a chair, walk straight 3 m, turn around, and return to seated position to assess the 180° walking-turn task. Turning performance was measured by two inertial sensor units of Physilog. Turn duration and steps were recorded for analysis. The numbers of retrospective and prospective falls were also obtained.

Results: No significant difference was observed between retrospective stroke fallers and non-fallers in turn duration and steps. Turn duration and steps were significantly greater in prospective stroke fallers than in non-fallers. The cutoff turn duration of 4 s (area under the curve 0.75, 95% CI: 0.56–0.93, sensitivity 67%, specificity 80%, p =.04) and turn step of 7 steps (area under the curve 0.73, 95% CI: 0.51–0.94, sensitivity 56%, specificity 85%, p =.05) were found to most accurately predict prospective stroke fallers from non-fallers.

Conclusions: Turn duration and steps were unable to discriminate between retrospective fallers and non-fallers but could predict prospective falls in patients with stroke. More than 4 s or 7 steps to complete a 180° turn while walking can be a predictor for patients with stroke at an increased risk of falling.     

预测年轻乳腺癌患者生存率列线图的建立和验证

目的:构建预测年轻乳腺癌患者生存情况的列线图，以期帮助临床诊疗。方法:收集SEER数据库中5 525例年轻乳腺癌患者的临床信息，通过单因素Log-rank检验和多因素Cox生存分析筛选出独立预后因素,用于构建预测患者3、5年总生存率(overall survival,OS)和癌症特异性生存率（cancer special survival,CSS)的列线图，将我院就诊的147例年轻乳腺癌患者作为验证集进行外部验证。结果:单因素和多因素分析结果显示，种族、病理类型、组织学分级、T分期、N分期、M分期、ER状态、HER-2状态、手术方式是与患者OS和CSS相关的独立危险因素，将这些因素纳入并建立预测患者OS和CSS的列线图模型。内部和外部验证结果显示模型具有良好的预测性能。基于建立的OS和CSS列线图模型对患者进行了风险分层，能够准确地将年轻乳腺癌患者分成预后有显著差异的三个风险亚组。结论:本研究构建的预测模型能较为准确的预测年轻乳腺癌患者的预后情况，为临床的诊疗提供科学依据。     

Risk factors related to the severity of COVID-19 in Wuhan

Objective: To evaluate the characteristics at admission of patients with moderate COVID-19 in Wuhan and to explore risk factors associated with the severe prognosis of the disease for prognostic prediction.Methods: In this retrospective study, moderate and severe disease was defined according to the report of the WHO-China Joint Mission on COVID-19. Clinical characteristics and laboratory findings of 172 patients with laboratory-confirmed moderate COVID-19 were collected when they were admitted to the Cancer Center of Wuhan Union Hospital between February 13, 2020 and February 25, 2020. This cohort was followed to March 14, 2020. The outcomes, being discharged as mild cases or developing into severe cases, were categorized into two groups. The data were compared and analyzed with univariate logistic regression to identify the features that differed significantly between the two groups. Based on machine learning algorithms, a further feature selection procedure was performed to identify the features that can contribute the most to the prediction of disease severity.Results: Of the 172 patients, 112 were discharged as mild cases, and 60 developed into severe cases. Four clinical characteristics and 18 laboratory findings showed significant differences between the two groups in the statistical test (P<0.01) and univariate logistic regression analysis (P<0.01). In the further feature selection procedure, six features were chosen to obtain the best performance in discriminating the two groups with a linear kernel support vector machine. The mean accuracy was 91.38%, with a sensitivity of 0.90 and a specificity of 0.94. The six features included interleukin-6, high-sensitivity cardiac troponin I, procalcitonin, high-sensitivity C-reactive protein, chest distress and calcium level.Conclusions: With the data collected at admission, the combination of one clinical characteristic and five laboratory findings contributed the most to the discrimination between the two groups with a linear kernel support vector machine classifier. These factors may be risk factors that can be used to perform a prognostic prediction regarding the severity of the disease for patients with moderate COVID-19 in the early stage of the disease.