ASPECT scores of patients with focal intracerebral hemorrhage were correlated with their short- and medium-term functional outcomes

PURPOSE

The Alberta Stroke Program Early CT Score (ASPECTS) is widely used to guide thrombolytic therapy and predict the functional outcome of patients with acute ischemic stroke (AIS). Whether ASPECTS can predict the functional outcome of patients with intracerebral hemorrhage (ASPECTS-H) remains unclear.

METHODS

Patients with primary intracerebral hemorrhage (ICH) were collected and retrospectively analyzed. ASPECTS-H was assessed at admission. Patients were followed up at 30 days and 90 days after the onset of ICH. Occurrence of death within 90 days after ICH was the primary endpoint. Modified Rankin Scale (mRS) ≥ 3 was considered a poor functional outcome.

RESULTS

A total of 149 patients met eligibility criteria; 61 (40.9%) had poor functional outcome at 30 days, and 37 (24.8%) had poor functional outcome at 90 days. Using binary logistic regression modeling, we found that a low ASPECTS-H was associated with a poor functional outcome. The risk ratio of a low ASPECTS-H was 2.31 at 30 days (P = 0.000; 95% CI, 1.560-3.421) and 2.711 at 90 days (P = 0.000; 95% CI, 1.677-4.381). The optimal cutoff value of ASPECTS-H to discriminate good and poor 30-day and 90-day outcomes was 7.5 (Sensitivity_30-day = 0.636, 1-Specificity_{30 - day} = 0.311; Sensitivity_90-day = 0.580, 1-Specificity_90-day = 0.270).

CONCLUSIONS

A low ASPECTS-H was an indicator of poor short-term and long-term functional outcomes of ICH.

Validation of E-PRE-DELIRIC in cardiac surgical ICU delirium: A retrospective cohort study

BACKGROUND

The early prediction model for delirium in intensive care units (ICUs)-E-PRE-DELIRIC-has been created to predict delirium development during the length of stay in ICUs. However, there have been few early predictive models for delirium in the cardiac surgical ICU (CSICU), and the predictive ability of the E-PRE-DELIRIC among patients following cardiac surgeries is still unknown.

AIMS AND OBJECTIVES

To validate the performance of E-PRE-DELIRIC in CSICU.

DESIGN

A retrospective cohort study.

METHODS

Data were retrospectively extracted from the electronic records for patients admitted in CSICU from January 2018 to December 2018 in a tertiary teaching hospital in China. Adult patients were included following the criteria of the E-PRE-DELIRIC model. Predictors, including age, history of cognitive impairment, history of alcohol abuse, urgent admission, use of corticosteroids, respiratory failure, blood urea nitrogen, and mean arterial pressure, at the time of ICU admission were retrieved, and delirium was assessed twice a day using the Confusion Assessment Method for the ICU. The performance of the E-PRE-DELIRIC model was evaluated by area under receiver operator characteristic curve, precision-recall curve (AUPRC), Hosmer-Lemeshow (HL) test, and calibration belt.

RESULTS

Of the 725 patients included, 120 (16.6%) developed delirium. The AUROC was 0.54 (95% confidence interval [CI], 0.48-0.59), and the AUPRC was 0.18 (95% CI, 0.12-0.20). The HL test showed a significant difference between predicted probability and delirium occurrence (χ² = 17.326, P = .027), and the overestimation chance of the E-PRE-DELIRIC score was 0.24 to 0.43.

CONCLUSION

The E-PRE-DELIRIC model has poor-to-fair predictive value in this study; thus, its application among the CSICU patients is limited. Development of reliable and validated tools for early prediction of delirium in CSICU is required.

RELEVANCE TO CLINICAL PRACTICE

Early prediction of delirium risk at CSICU admission is of vital importance and could provide timely information to caregivers. However, the E-PRE-DELIRIC model should be applied cautiously in the CSICU because of the significant probability of over-estimating the risk of developing delirium.

Accuracy of the Palliative Prognostic Score With or Without Clinicians' Prediction of Survival in Patients With Far Advanced Cancer

CONTEXT

Previous studies suggest that clinicians' prediction of survival (CPS) may have reduced the accuracy of objective indicators for prognostication in palliative care.

OBJECTIVES

We aimed to examine the accuracy of CPS alone, compared to the original Palliative Prognostic Score (PaP), and five clinical/laboratory variables of the PaP in patients with far advanced cancer.

METHODS

We compared the discriminative accuracy of three prediction models (the PaP-CPS [the score of the categorical CPS of PaP], PaP without CPS [sum of the scores of only the objective variables of PaP], and PaP total score) across 3 settings: inpatient palliative care consultation team, palliative care unit, and home palliative care. We computed the area under receiver operating characteristic curve (AUROC) for 30-day survival and concordance index (C-index) to compare the discriminative accuracy of these three models.

RESULTS

We included a total of 1534 subjects with median survival of 34.0 days. The AUROC and C-index in the three settings were 0.816-0.896 and 0.732-0.799 for the PaP total score, 0.808-0.884 and 0.713-0.782 for the PaP-CPS, and 0.726-0.815 and 0.672-0.728 for the PaP without CPS, respectively. The PaP total score and PaP-CPS showed similar AUROCs and C-indices across the three settings. The PaP total score had significantly higher AUROCs and C-indices than the PaP without CPS across the three settings.

CONCLUSION

Overall, the PaP total score, PaP-CPS, and PaP without CPS showed good discriminative performances. However, the PaP total score and PaP-CPS were significantly more accurate than the PaP without CPS.

Risk Prediction of Cognitive Decline after Stroke

BACKGROUND AND PURPOSE

Cognitive decline is one of the major outcomes after stroke. We have developed and evaluated a risk predictive tool of post-stroke cognitive decline and assessed its clinical utility.

METHODS

In this population-based cohort, 4,783 patients with first-ever stroke from the South London Stroke Register (1995-2010) were included in developing the model. Cognitive impairment was measured using the Mini Mental State Examination (cut off 24/30) and the Abbreviated Mental Test (cut off 8/10) at 3-months and yearly thereafter. A penalised mixed-effects linear model was developed and temporal-validated in a new cohort consisted of 1,718 stroke register participants recruited from (2011-2018). Prediction errors on discrimination and calibration were assessed. The clinical utility of the model was evaluated using prognostic accuracy measurements and decision curve analysis.

RESULTS

The overall predictive model showed good accuracy, with root mean squared error of 0.12 and R2 of 73%. Good prognostic accuracy for predicting severe cognitive decline was observed AUC: (88%, 95% CI [85-90]), (89.6%, 95% CI [86-92]), (87%, 95% CI [85-91]) at 3 months, one and 5 years respectively. Average predicted recovery patterns were analysed by age, stroke subtype, Glasgow-coma scale, and left-stroke and showed variability. DECISION: curve analysis showed an increased clinical benefit, particularly at threshold probabilities of above 15% for predictive risk of cognitive impairment.

CONCLUSIONS

The derived prognostic model seems to accurately screen the risk of post-stroke cognitive decline. Such prediction could support the development of more tailored management evaluations and identify groups for further study and future trials.

Erratum: Improving the Prediction of Benign or Malignant Breast Masses Using a Combination of Image Biomarkers and Clinical Parameters

[This corrects the article DOI: 10.3389/fonc.2021.629321.].

Dr. Agent: Clinical predictive model via mimicked second opinions

OBJECTIVE

Prediction of disease phenotypes and their outcomes is a difficult task. In practice, patients routinely seek second opinions from multiple clinical experts for complex disease diagnosis. Our objective is to mimic such a practice of seeking second opinions by training 2 agents with different focuses: the primary agent studies the most recent visit of the patient to learn the current health status, and then the second-opinion agent considers the entire patient history to obtain a more global view.

MATERIALS AND METHODS

Our approach Dr. Agent augments recurrent neural networks with 2 policy gradient agents. Moreover, Dr. Agent is customized with various patient demographics information and learns a dynamic skip connection to focus on the relevant information over time. We trained Dr. Agent to perform 4 clinical prediction tasks on the publicly available MIMIC-III (Medical Information Mart for Intensive Care) database: (1) in-hospital mortality prediction, (2) acute care phenotype classification, (3) physiologic decompensation prediction, and (4) forecasting length of stay. We compared the performance of Dr. Agent against 4 baseline clinical predictive models.

RESULTS

Dr. Agent outperforms baseline clinical prediction models across all 4 tasks in terms of all metrics. Compared with the best baseline model, Dr. Agent achieves up to 15% higher area under the precision-recall curve on different tasks.

CONCLUSIONS

Dr. Agent can comprehensively model the long-term dependencies of patients' health status while considering patients' demographics using 2 agents, and therefore achieves better prediction performance on different clinical prediction tasks.

How good are clinicians in predicting the presence of Pseudomonas spp. in diabetic foot infections? A prospective clinical evaluation

Introduction

The most frequently prescribed empirical antibiotic agents for mild and moderate diabetic foot infections (DFIs) are amino-penicillins and second-generation cephalosporins that do not cover Pseudomonas spp. Many clinicians believe they can predict the involvement of Pseudomonas in a DFI by visual and/or olfactory clues, but no data support this assertion.

Methods

In this prospective observational study, we separately asked 13 experienced (median 11 years) healthcare workers whether they thought the Pseudomonas spp. would be implicated in the DFI. Their predictions were compared with the results of cultures of deep/intraoperative specimens and/or the clinical remission of DFI achieved with antibiotic agents that did not cover Pseudomonas.

Results

Among 221 DFI episodes in 88 individual patients, intraoperative tissue cultures grew Pseudomonas in 22 cases (10%, including six bone samples). The presence of Pseudomonas was correctly predicted with a sensitivity of 0.32, specificity of 0.84, positive predictive value of 0.18 and negative predictive value 0.92. Despite two feedbacks of the interim results and a 2-year period, the clinicians' predictive performance did not improve.

Conclusion

The combined visual and olfactory performance of experienced clinicians in predicting the presence of Pseudomonas in a DFI was moderate, with better specificity than sensitivity, and did not improve over time. Further investigations are needed to determine whether clinicians should use a negative prediction of the presence of Pseudomonas in a DFI, especially in settings with a high prevalence of pseudomonal DFIs.

Improving the Prediction of Benign or Malignant Breast Masses Using a Combination of Image Biomarkers and Clinical Parameters

Background: Breast cancer is one of the leading causes of death in female cancer patients. The disease can be detected early using Mammography, an effective X-ray imaging technology. The most important step in mammography is the classification of mammogram patches as benign or malignant. Classically, benign or malignant breast tumors are diagnosed by radiologists' interpretation of mammograms based on clinical parameters. However, because masses are heterogeneous, clinical parameters supply limited information on mammography mass. Therefore, this study aimed to predict benign or malignant breast masses using a combination of image biomarkers and clinical parameters. Methods: We trained a deep learning (DL) fusion network of VGG16 and Inception-V3 network in 5,996 mammography images from the training cohort; DL features were extracted from the second fully connected layer of the DL fusion network. We then developed a combined model incorporating DL features, hand-crafted features, and clinical parameters to predict benign or malignant breast masses. The prediction performance was compared between clinical parameters and the combination of the above features. The strengths of the clinical model and the combined model were subsequently validated in a test cohort (n = 244) and an external validation cohort (n = 100), respectively. Results: Extracted features comprised 30 hand-crafted features, 27 DL features, and 5 clinical features (shape, margin type, breast composition, age, mass size). The model combining the three feature types yielded the best performance in predicting benign or malignant masses (AUC = 0.961) in the test cohort. A significant difference in the predictive performance between the combined model and the clinical model was observed in an independent external validation cohort (AUC: 0.973 vs. 0.911, p = 0.019). Conclusion: The prediction of benign or malignant breast masses improves when image biomarkers and clinical parameters are combined; the combined model was more robust than clinical parameters alone.

Enhancing the prediction of childhood asthma remission: Integrating clinical factors with microRNAs

The novel integration of baseline clinical and microRNA variables significantly improves the long-term individualized prediction of childhood asthma remission by early adulthood compared to using clinical variables alone.

In vitro to Clinical Translation of Combinatorial Effects of Doxorubicin and Abemaciclib in Rb-Positive Triple Negative Breast Cancer: A Systems-Based Pharmacokinetic/Pharmacodynamic Modeling Approach

Background

Doxorubicin (DOX) and its pegylated liposomal formulation (L_DOX) are the standard of care for triple-negative breast cancer (TNBC). However, resistance to DOX often occurs, motivating the search for alternative treatment approaches. The retinoblastoma protein (Rb) is a potential pharmacological target for TNBC treatment since its expression has been associated with resistance to DOX-based therapy.

Methods

DOX (0.01–20 μM) combination with abemaciclib (ABE, 1–6 μM) was evaluated over 72 hours on Rb-positive (MDA-MB-231) and Rb-negative (MDA-MB-468) TNBC cells. Combination indices (CI) for DOX+ABE were calculated using Compusyn software. The TNBC cell viability time-course and fold-change from the control of phosphorylated-Rb (pRb) protein expression were measured with CCK8-kit and enzyme-linked immunosorbent assay. A cell-based pharmacodynamic (PD) model was developed, where pRb protein dynamics drove cell viability response. Clinical pharmacokinetic (PK) models for DOX, L_DOX, and ABE were developed using data extracted from the literature. After scaling cancer cell growth to clinical TNBC tumor growth, the time-to-tumor progression (TTP) was predicted for human dosing regimens of DOX, ABE, and DOX+ABE.

Results

DOX and ABE combinations were synergistic (CI<1) in MDA-MB-231 and antagonistic (CI>1) in MDA-MB-468. The maximum inhibitory effects (Imax) for both drugs were set to one. The drug concentrations producing 50% of Imax for DOX and ABE were 0.565 and 2.31 μM (MDA-MB-231) and 0.121 and 1.61 μM (MDA-MB-468). The first-orders rate constants of abemaciclib absorption (k_a) and doxorubicin release from L_DOX (k_Rel) were estimated at 0.31 and 0.013 h⁻¹. Their linear clearances were 21.7 (ABE) and 32.1 L/h (DOX). The estimated TTP for intravenous DOX (75 mg/m2 every 21 days), intravenous L_DOX (50 mg/m² every 28 days), and oral ABE (200 mg twice a day) were 125, 31.2, and 8.6 days shorter than drug-free control. The TTP for DOX+ABE and L_DOX+ABE were 312 days and 47.5 days shorter than control, both larger than single-agent DOX, suggesting improved activity with the DOX+ABE combination.

Conclusion

The developed translational systems-based PK/PD model provides an in vitro-to-clinic modeling platform for DOX+ABE in TNBC. Although model-based simulations suggest improved outcomes with combination over monotherapy, tumor relapse was not prevented with the combination. Hence, DOX+ABE may not be an effective treatment combination for TNBC.

Construction and validation of a nomogram for patients with skin cancer

Skin cancer is a common malignant tumor in human beings. At present, the construction of clinical prediction models mainly focuses on malignant melanoma and no researchers have constructed clinical prediction models for all kind of skin cancer to predict the prognosis of skin cancer. We used patient data collected from the surveillance, epidemiology, and end results program database to construct and validate our model for clinical prediction of skin cancer, hoping to provide a reference for clinical treatment of skin cancer.R software was used for univariate and multivariate Cox regression analysis of variables to screen out factors that have an impact on the survival of skin cancer patients. Then the prognostic model of skin cancer patients was constructed and the nomogram was drawn. Concordance Index (C-index), receiver operating characteristic (ROC) curve and calibration curve were used to evaluate the clinical prediction model.A total of 3180 skin cancer patients were included in this study. We constructed nomogram, a 3-year and 5-year clinical prediction model for skin cancer patients. We used C-index to evaluate the accuracy of nomogram model, and the result of C-index was 0.728, 95%CI (0.703-0.753). The nomogram model was evaluated by ROC curve. The area under the curve values of the ROC curve for 3-year survival rate and 5-year survival rate were 0.732 and 0.768 respectively. The model calibration diagram of the modeling group also shows that the model exhibits high accuracy.The nomogram model of postoperative survival of patients with skin cancer, based on the surveillance, epidemiology, and end results program database of patients with skin cancer, has shown good stability and accuracy in multi-method validation.

Assessing the Usefulness of the Prevexair Smartphone Application in the Follow-Up High-Risk Patients with COPD

Introduction

This manuscript analyzes the exacerbations recorded by the Prevexair application through the daily analysis of symptoms in high-risk patients with COPD and explores its usefulness in assessing clinical stability with respect to that reported in visits.

Patients and Methods

This study is a multi-centre cohort of COPD patients with the exacerbator phenotype who were monitored over 6 months. The Prevexair application was installed on the patients' smartphones. Patients used the app to record symptom changes, use of medication and use of healthcare resources. It is not established a recommended action plan when worsening of symptoms. At their clinical visit during the follow-up period, patients were asked about exacerbations suffered during these 6 months of monitoring. The investigators who conducted the visit were blinded about the Prevexair app records.

Results

The patients experienced a total of 185 exacerbations according to daily records in the app whereas only 64 exacerbations were recalled during medical visits. Perception became more accurate for severe exacerbations (kappa 0.6577), although we found no factors that predicted poor recall. The proportion of 72.5% patients were classified as unstable if the exacerbations captured by Prevexair were used to define stability, versus 47.8% if the exacerbations recall in visit was used. Two-thirds of the exacerbations recorded in the Prevexair application were not reported to doctors during their clinical visits. Almost half were treated with oral corticosteroids and/or antibiotics and more than one-quarter of the exacerbations treated did not seek medical attention.

Conclusion

The findings of this cohort study confirm that patients do not always remember the exacerbations suffered during their medical visit. The prevexair application is useful in monitoring COPD patients at high risk, in order to a better assessment of exacerbations of COPD during medical visits. Further research must be carried out to evaluate this strategy in clinical practice.

Deep Learning Improves Osteonecrosis Prediction of Femoral Head After Internal Fixation Using Hybrid Patient and Radiograph Variables

Femoral neck fractures (FNFs) are a great public health problem that leads to a high incidence of death and dysfunction. Osteonecrosis of the femoral head (ONFH) after internal fixation of FNF is a frequently reported complication and a major cause for reoperation. Early intervention can prevent osteonecrosis aggravation at the preliminary stage. However, at present, failure to diagnose asymptomatic ONFH after FNF fixation hinders effective intervention at early stages. The primary objective of this study was to develop a predictive model for postoperative ONFH using deep learning (DL) methods developed using plain X-ray radiographs and hybrid patient variables. A two-center retrospective study of patients who underwent closed reduction and cannulated screw fixation was performed. We trained a convolutional neural network (CNN) model using postoperative pelvic radiographs and the output regressive radiograph variables. A less experienced orthopedic doctor, and an experienced orthopedic doctor also evaluated and diagnosed the patients using postoperative pelvic radiographs. Hybrid nomograms were developed based on patient and radiograph variables to determine predictive performance. A total of 238 patients, including 95 ONFH patients and 143 non-ONFH patients, were included. A CNN model was trained using postoperative radiographs and output radiograph variables. The accuracy of the validation set was 0.873 for the CNN model, and the algorithm achieved an area under the curve (AUC) value of 0.912 for the prediction. The diagnostic and predictive ability of the algorithm was superior to that of the two doctors, based on the postoperative X-rays. The addition of DL-based radiograph variables to the clinical nomogram improved predictive performance, resulting in an AUC of 0.948 (95% CI, 0.920-0.976) and better calibration. The decision curve analysis showed that adding the DL increased the clinical usefulness of the nomogram compared with a clinical approach alone. In conclusion, we constructed a DL facilitated nomogram that incorporated a hybrid of radiograph and patient variables, which can be used to improve the prediction of preoperative osteonecrosis of the femoral head after internal fixation.

Clinical Predictive Models for COVID-19: Systematic Study

BACKGROUND

COVID-19 is a rapidly emerging respiratory disease caused by SARS-CoV-2. Due to the rapid human-to-human transmission of SARS-CoV-2, many health care systems are at risk of exceeding their health care capacities, in particular in terms of SARS-CoV-2 tests, hospital and intensive care unit (ICU) beds, and mechanical ventilators. Predictive algorithms could potentially ease the strain on health care systems by identifying those who are most likely to receive a positive SARS-CoV-2 test, be hospitalized, or admitted to the ICU.

OBJECTIVE

The aim of this study is to develop, study, and evaluate clinical predictive models that estimate, using machine learning and based on routinely collected clinical data, which patients are likely to receive a positive SARS-CoV-2 test or require hospitalization or intensive care.

METHODS

Using a systematic approach to model development and optimization, we trained and compared various types of machine learning models, including logistic regression, neural networks, support vector machines, random forests, and gradient boosting. To evaluate the developed models, we performed a retrospective evaluation on demographic, clinical, and blood analysis data from a cohort of 5644 patients. In addition, we determined which clinical features were predictive to what degree for each of the aforementioned clinical tasks using causal explanations.

RESULTS

Our experimental results indicate that our predictive models identified patients that test positive for SARS-CoV-2 a priori at a sensitivity of 75% (95% CI 67%-81%) and a specificity of 49% (95% CI 46%-51%), patients who are SARS-CoV-2 positive that require hospitalization with 0.92 area under the receiver operator characteristic curve (AUC; 95% CI 0.81-0.98), and patients who are SARS-CoV-2 positive that require critical care with 0.98 AUC (95% CI 0.95-1.00).

CONCLUSIONS

Our results indicate that predictive models trained on routinely collected clinical data could be used to predict clinical pathways for COVID-19 and, therefore, help inform care and prioritize resources.

Clinical Features to Predict 22q11.2 Deletion Syndrome Proven by Molecular Genetic Testing

The 22q11.2 deletion syndrome (22q11.2 DS) is the most common microdeletion syndrome with a wide variety of clinical features. However, as there are no clinical criteria for diagnosis, confirmation is solely done by genetic tests if clinicians recognize the syndrome. Therefore, we aimed to identify clinical features that may help clinicians recognize 22q11.2 DS. Participants with at least two anomalies were enrolled, complete patient history and physical examinations were performed, then multiplex ligation-dependent probe amplification (MLPA) analysis for 22q11.2 DS was utilized. We identified 11/48 (23%) cases with 22q11.2 DS. Palatal anomalies, hypocalcemia, and ≥3 affected body systems were highly significant presentations in the 22q11.2 DS group versus the group without deletion ( p  < 0.05). Therefore, a comprehensive physical examination is crucial at identifying any subtle features which may lead to testing and a definite diagnosis.

Reporting quality of studies using machine learning models for medical diagnosis: a systematic review

AIMS

We conducted a systematic review assessing the reporting quality of studies validating models based on machine learning (ML) for clinical diagnosis, with a specific focus on the reporting of information concerning the participants on which the diagnostic task was evaluated on.

METHOD

Medline Core Clinical Journals were searched for studies published between July 2015 and July 2018. Two reviewers independently screened the retrieved articles, a third reviewer resolved any discrepancies. An extraction list was developed from the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis guideline. Two reviewers independently extracted the data from the eligible articles. Third and fourth reviewers checked, verified the extracted data as well as resolved any discrepancies between the reviewers.

RESULTS

The search results yielded 161 papers, of which 28 conformed to the eligibility criteria. Detail of data source was reported in 24 of the 28 papers. For all of the papers, the set of patients on which the ML-based diagnostic system was evaluated was partitioned from a larger dataset, and the method for deriving such set was always reported. Information on the diagnostic/non-diagnostic classification was reported well (23/28). The least reported items were the use of reporting guideline (0/28), distribution of disease severity (8/28 patient flow diagram (10/28) and distribution of alternative diagnosis (10/28). A large proportion of studies (23/28) had a delay between the conduct of the reference standard and ML tests, while one study did not and four studies were unclear. For 15 studies, it was unclear whether the evaluation group corresponded to the setting in which the ML test will be applied to.

CONCLUSION

All studies in this review failed to use reporting guidelines, and a large proportion of them lacked adequate detail on participants, making it difficult to replicate, assess and interpret study findings.

PROSPERO REGISTRATION NUMBER

CRD42018099167.

Prediction of lithium response using clinical data

OBJECTIVE

Promptly establishing maintenance therapy could reduce morbidity and mortality in patients with bipolar disorder. Using a machine learning approach, we sought to evaluate whether lithium responsiveness (LR) is predictable using clinical markers.

METHOD

Our data are the largest existing sample of direct interview-based clinical data from lithium-treated patients (n = 1266, 34.7% responders), collected across seven sites, internationally. We trained a random forest model to classify LR-as defined by the previously validated Alda scale-against 180 clinical predictors.

RESULTS

Under appropriate cross-validation procedures, LR was predictable in the pooled sample with an area under the receiver operating characteristic curve of 0.80 (95% CI 0.78-0.82) and a Cohen kappa of 0.46 (0.4-0.51). The model demonstrated a particularly low false-positive rate (specificity 0.91 [0.88-0.92]). Features related to clinical course and the absence of rapid cycling appeared consistently informative.

CONCLUSION

Clinical data can inform out-of-sample LR prediction to a potentially clinically relevant degree. Despite the relevance of clinical course and the absence of rapid cycling, there was substantial between-site heterogeneity with respect to feature importance. Future work must focus on improving classification of true positives, better characterizing between- and within-site heterogeneity, and further testing such models on new external datasets.

A Tutorial on Mechanical Decision-Making for Personnel and Educational Selection

In decision-making, it is important not only to use the correct information but also to combine information in an optimal way. There are robust research findings that a mechanical combination of information for personnel and educational selection matches or outperforms a holistic combination of information. However, practitioners and policy makers seldom use mechanical combination for decision-making. One of the important conditions for scientific results to be used in practice and to be part of policy-making is that results are easily accessible. To increase the accessibility of mechanical judgment prediction procedures, we (1) explain in detail how mechanical combination procedures work, (2) provide examples to illustrate these procedures, and (3) discuss some limitations of mechanical decision-making.

Novel pediatric-automated respiratory score using physiologic data and machine learning in asthma

OBJECTIVES

Manual clinical scoring systems are the current standard used for acute asthma clinical care pathways. No automated system exists that assesses disease severity, time course, and treatment impact in pediatric acute severe asthma exacerbations.

WORKING HYPOTHESIS

machine learning applied to continuous vital sign data could provide a novel pediatric-automated asthma respiratory score (pARS) by using the manual pediatric asthma score (PAS) as the clinical care standard.

METHODS

Continuous vital sign monitoring data (heart rate, respiratory rate, and pulse oximetry) were merged with the health record data including a provider-determined PAS in children between 2 and 18 years of age admitted to the pediatric intensive care unit (PICU) for status asthmaticus. A cascaded artificial neural network (ANN) was applied to create an automated respiratory score and validated by two approaches. The ANN was compared with the Normal and Poisson regression models.

RESULTS

Out of an initial group of 186 patients, 128 patients met inclusion criteria. Merging physiologic data with clinical data yielded >37 000 data points for model training. The pARS score had good predictive accuracy, with 80% of the pARS values within ±2 points of the provider-determined PAS, especially over the mid-range of PASs (6-9). The Poisson and Normal distribution regressions yielded a smaller overall median absolute error.

CONCLUSIONS

The pARS reproduced the manually recorded PAS. Once validated and studied prospectively as a tool for research and for physician decision support, this methodology can be implemented in the PICU to objectively guide treatment decisions.

Submissions from the SPRINT Data Analysis Challenge on clinical risk prediction: a cross-sectional evaluation

OBJECTIVES

To collate and systematically characterise the methods, results and clinical performance of the clinical risk prediction submissions to the Systolic Blood Pressure Intervention Trial (SPRINT) Data Analysis Challenge.

DESIGN

Cross-sectional evaluation.

DATA SOURCES

SPRINT Challenge online submission website.

STUDY SELECTION

Submissions to the SPRINT Challenge for clinical prediction tools or clinical risk scores.

DATA EXTRACTION

In duplicate by three independent reviewers.

RESULTS

Of 143 submissions, 29 met our inclusion criteria. Of these, 23/29 (79%) reported prediction models for an efficacy outcome (20/23 [87%] of these used the SPRINT study primary composite outcome, 14/29 [48%] used a safety outcome, and 4/29 [14%] examined a combined safety/efficacy outcome). Age and cardiovascular disease history were the most common variables retained in 80% (12/15) of the efficacy and 60% (6/10) of the safety models. However, no two submissions included an identical list of variables intending to predict the same outcomes. Model performance measures, most commonly, the C-statistic, were reported in 57% (13/23) of efficacy and 64% (9/14) of safety model submissions. Only 2/29 (7%) models reported external validation. Nine of 29 (31%) submissions developed and provided evaluable risk prediction tools. Using two hypothetical vignettes, 67% (6/9) of the tools provided expected recommendations for a low-risk patient, while 44% (4/9) did for a high-risk patient. Only 2/29 (7%) of the clinical risk prediction submissions have been published to date.

CONCLUSIONS

Despite use of the same data source, a diversity of approaches, methods and results was produced by the 29 SPRINT Challenge competition submissions for clinical risk prediction. Of the nine evaluable risk prediction tools, clinical performance was suboptimal. By collating an overview of the range of approaches taken, researchers may further optimise the development of risk prediction tools in SPRINT-eligible populations, and our findings may inform the conduct of future similar open science projects.

Prediction of skin anti-aging clinical benefits of an association of ingredients from marine and maritime origins: Ex vivo evaluation using a label-free quantitative proteomic and customized data processing approach

BACKGROUND

The application of ingredients from marine and maritime origins is increasingly common in skin care products, driven by consumer expectations for natural ingredients. However, these ingredients are typically studied for a few isolated in vitro activities.

OBJECTIVES

The purpose of this study was to carry out a comprehensive evaluation of the activity on the skin of an association of ingredients from marine and maritime origins using label-free quantitative proteomic analysis, in order to predict the clinical benefits if used in a skin care product.

METHODS

An aqueous gel containing 6.1% of ingredients from marine and maritime origins (amino acid-enriched giant kelp extract, trace element-enriched seawater, dedifferentiated sea fennel cells) was topically applied on human skin explants. The skin explants' proteome was analyzed in a label-free manner by high-performance liquid nano-chromatography coupled with tandem mass spectrometry. A specific data processing pipeline (CORAVALID) providing an objective and comprehensive interpretation of the statistically relevant biological activities processed the results.

RESULTS

Compared to untreated skin explants, 64 proteins were significantly regulated by the gel treatment (q-value ≤ 0.05). Computer data processing revealed an activity of the ingredients on the epidermis and the dermis. These significantly regulated proteins are involved in gene expression, cell survival and metabolism, inflammatory processes, dermal extracellular matrix synthesis, melanogenesis and keratinocyte proliferation, migration, and differentiation.

CONCLUSIONS

These results suggest that the tested ingredients could help to preserve a healthy epidermis and dermis, and possibly to prevent the visible signs of skin aging.

Calibration drift in regression and machine learning models for acute kidney injury

Objective

Predictive analytics create opportunities to incorporate personalized risk estimates into clinical decision support. Models must be well calibrated to support decision-making, yet calibration deteriorates over time. This study explored the influence of modeling methods on performance drift and connected observed drift with data shifts in the patient population.

Materials and Methods

Using 2003 admissions to Department of Veterans Affairs hospitals nationwide, we developed 7 parallel models for hospital-acquired acute kidney injury using common regression and machine learning methods, validating each over 9 subsequent years.

Results

Discrimination was maintained for all models. Calibration declined as all models increasingly overpredicted risk. However, the random forest and neural network models maintained calibration across ranges of probability, capturing more admissions than did the regression models. The magnitude of overprediction increased over time for the regression models while remaining stable and small for the machine learning models. Changes in the rate of acute kidney injury were strongly linked to increasing overprediction, while changes in predictor-outcome associations corresponded with diverging patterns of calibration drift across methods.

Conclusions

Efficient and effective updating protocols will be essential for maintaining accuracy of, user confidence in, and safety of personalized risk predictions to support decision-making. Model updating protocols should be tailored to account for variations in calibration drift across methods and respond to periods of rapid performance drift rather than be limited to regularly scheduled annual or biannual intervals.

Prospective Validation of a Screening Tool to Identify Older Adults in Need of a Driving Evaluation

OBJECTIVES

To prospectively validate and refine the 5-item "CRASH" screening tool for identifying older drivers needing a behind-the-wheel (BTW) test.

DESIGN

Prospective observational study.

SETTING

Geriatric and internal medicine primary care clinics affiliated with a tertiary care hospital and a local BTW program.

PARTICIPANTS

Cognitively intact drivers aged 65 and older (N = 315).

MEASUREMENTS

Participants completed baseline questionnaire (including CRASH tool) and assessments and BTW test (evaluator blinded to questionnaire results) and participated in 1-month telephone follow-up. Analysis included descriptive statistics and examination of predictive ability of the CRASH tool to discriminate normal (pass) from abnormal (conditional pass or fail) on the BTW test, with logistic regression and CART techniques for tool refinement.

RESULTS

Two hundred sixty-six participants (84%) had a BTW test; of these, 17% had a normal rating and 83% an abnormal rating. Forty-five percent of those with an abnormal score were advised to limit driving under particular conditions. Neither the CRASH tool nor its individual component variables were significantly associated with the summary BTW score; in refined models with other variables, the best-performing tool had approximately 67% sensitivity and specificity for an abnormal BTW score. Most participants found the BTW test useful and were willing to pay a median of $50. At 1-month follow-up, no participants had stopped driving.

CONCLUSION

The CRASH screening tool cannot be recommended for use in clinical practice. Findings on older adults' perceived utility of the BTW test and the stability of driving patterns at 1-month follow-up could be useful for future research studies and for design of older driver programs.

Reliability and Utility of the Surprise Question in CKD Stages 4 to 5

BACKGROUND

Prognostic uncertainty is one barrier to engaging in goals-of-care discussions in chronic kidney disease (CKD). The surprise question ("Would you be surprised if this patient died in the next 12 months?") is a tool to assist in prognostication. However, it has not been studied in non-dialysis-dependent CKD and its reliability is unknown.

STUDY DESIGN

Observational study.

SETTING & PARTICIPANTS

388 patients at least 60 years of age with non-dialysis-dependent CKD stages 4 to 5 who were seen at an outpatient nephrology clinic.

PREDICTOR

Trinary (ie, Yes, Neutral, or No) and binary (Yes or No) surprise question response.

OUTCOMES

Mortality, test-retest reliability, and blinded inter-rater reliability.

MEASUREMENTS

Baseline comorbid conditions, Charlson Comorbidity Index, cause of CKD, and baseline laboratory values (ie, serum creatinine/estimated glomerular filtration rate, serum albumin, and hemoglobin).

RESULTS

Median patient age was 71 years with median follow-up of 1.4 years, during which time 52 (13%) patients died. Using the trinary surprise question, providers responded Yes, Neutral, and No for 202 (52%), 80 (21%), and 106 (27%) patients, respectively. About 5%, 15%, and 27% of Yes, Neutral, and No patients died, respectively (P<0.001). Trinary surprise question inter-rater reliability was 0.58 (95% CI, 0.42-0.72), and test-retest reliability was 0.63 (95% CI, 0.54-0.72). The trinary surprise question No response had sensitivity and specificity of 55% and 76%, respectively (95% CIs, 38%-71% and 71%-80%, respectively). The binary surprise question had sensitivity of 66% (95% CI, 49%-80%; P=0.3 vs trinary), but lower specificity of 68% (95% CI, 63%-73%; P=0.02 vs trinary).

LIMITATIONS

Single center, small number of deaths.

CONCLUSIONS

The surprise question associates with mortality in CKD stages 4 to 5 and demonstrates moderate to good reliability. Future studies should examine how best to deploy the surprise question to facilitate advance care planning in advanced non-dialysis-dependent CKD.

The accuracy of clinicians' predictions of survival in advanced cancer: a review

The process of formulating an accurate survival prediction is often difficult but important, as it influences the decisions of clinicians, patients, and their families. The current article aims to review the accuracy of clinicians' predictions of survival (CPS) in advanced cancer patients. A literature search of Cochrane CENTRAL, EMBASE, and MEDLINE was conducted to identify studies that reported clinicians' prediction of survival in advanced cancer patients. Studies were included if the subjects consisted of advanced cancer patients and the data reported on the ability of clinicians to predict survival, with both estimated and observed survival data present. Studies reporting on the ability of biological and molecular markers to predict survival were excluded. Fifteen studies that met the inclusion and exclusion criteria were identified. Clinicians in five studies underestimated patients' survival (estimated to observed survival ratio between 0.5 and 0.92). In contrast, 12 studies reported clinicians' overestimation of survival (ratio between 1.06 and 6). CPS in advanced cancer patients is often inaccurate and overestimated. Given these findings, clinicians should be aware of their tendency to be overoptimistic. Further investigation of predictive patient and clinician characteristics is warranted to improve clinicians' ability to predict survival.