Development and validation of a multicenter Cox regression model to predict all-cause mortality in patients with renal masses suspicious for renal cancer

OBJECTIVE

Life expectancy models are useful tools to support clinical decision-making. Prior models have not been used widely in clinical practice for patients with renal masses. We sought to develop and validate a model to predict life expectancy following the detection of a localized renal mass suspicious for renal cell carcinoma.

MATERIALS AND METHODS

Using retrospective data from 2 large centers, we identified patients diagnosed with clinically localized renal parenchymal masses from 1998 to 2018. After 2:1 random sampling into a derivation and validation cohort stratified by site, we used age, sex, log-transformed tumor size, simplified cardiovascular index and planned treatment to fit a Cox regression model to predict all-cause mortality from the time of diagnosis. The model's discrimination was evaluated using a C-statistic, and calibration was evaluated visually at 1, 5, and 10 years.

RESULTS

We identified 2,667 patients (1,386 at Corewell Health and 1,281 at Johns Hopkins) with renal masses. Of these, 420 (16%) died with a median follow-up of 5.2 years (interquartile range 2.2-8.3). Statistically significant predictors in the multivariable Cox regression model were age (hazard ratio [HR] 1.04; 95% confidence interval [CI] 1.03-1.05); male sex (HR 1.40; 95% CI 1.08-1.81); log-transformed tumor size (HR 1.71; 95% CI 1.30-2.24); cardiovascular index (HR 1.48; 95% CI 1.32-1.67), and planned treatment (HR: 0.10, 95% CI: 0.06-0.18 for kidney-sparing intervention and HR: 0.20, 95% CI: 0.11-0.35 for radical nephrectomy vs. no intervention). The model achieved a C-statistic of 0.74 in the derivation cohort and 0.73 in the validation cohort. The model was well-calibrated at 1, 5, and 10 years of follow-up.

CONCLUSIONS

For patients with localized renal masses, accurate determination of life expectancy is essential for decision-making regarding intervention vs. active surveillance as a primary treatment modality. We have made available a simple tool for this purpose.

Emergence of Long-Range Angular Correlations in Low-Multiplicity Proton-Proton Collisions

This Letter presents the measurement of near-side associated per-trigger yields, denoted ridge yields, from the analysis of angular correlations of charged hadrons in proton-proton collisions at sqrt[s]=13  TeV. Long-range ridge yields are extracted for pairs of charged particles with a pseudorapidity difference of 1.4<|Δη|<1.8 and a transverse momentum of 1 Collapse

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First Measurement of the |t| Dependence of Incoherent J/ψ Photonuclear Production

The first measurement of the cross section for incoherent photonuclear production of J/ψ vector mesons as a function of the Mandelstam |t| variable is presented. The measurement was carried out with the ALICE detector at midrapidity, |y|<0.8, using ultraperipheral collisions of Pb nuclei at a center-of-mass energy per nucleon pair of sqrt[s_{NN}]=5.02  TeV. This rapidity interval corresponds to a Bjorken-x range (0.3-1.4)×10^{-3}. Cross sections are given in five |t| intervals in the range 0.04<|t|<1  GeV^{2} and compared to the predictions by different models. Models that ignore quantum fluctuations of the gluon density in the colliding hadron predict a |t| dependence of the cross section much steeper than in data. The inclusion of such fluctuations in the same models provides a better description of the data.

External Validation of Postpartum Hemorrhage Prediction Models Using Electronic Health Record Data

OBJECTIVE

 A recent study leveraging machine learning methods found that postpartum hemorrhage (PPH) can be predicted accurately at the time of labor admission in the U.S. Consortium for Safe Labor (CSL) dataset, with a C-statistic as high as 0.93. These CSL models were developed in older data (2002-2008) and used an estimated blood loss (EBL) of ≥1,000 mL to define PPH. We sought to externally validate these models using a more recent cohort of births where blood loss was measured using quantitative blood loss (QBL) methods.

STUDY DESIGN

 Using data from 5,261 deliveries between February 1, 2019 and May 11, 2020 at a single tertiary hospital, we mapped our electronic health record (EHR) data to the 55 predictors described in previously published CSL models. PPH was defined as QBL ≥1,000 mL within 24 hours after delivery. Model discrimination and calibration of the four CSL models were measured using our cohort. In a secondary analysis, we fit new models in our study cohort using the same predictors and algorithms as the original CSL models.

RESULTS

 The original study cohort had a substantially lower rate of PPH, 4.8% (7,279/228,438) versus 25% (1,321/5,261), possibly due to differences in measurement. The CSL models had lower discrimination in our study cohort, with a C-statistic as high as 0.57 (logistic regression). Models refit in our study cohort achieved better discrimination, with a C-statistic as high as 0.64 (random forest). Calibration improved in the refit models as compared with the original models.

CONCLUSION

 The CSL models' accuracy was lower in a contemporary EHR where PPH is assessed using QBL. As institutions continue to adopt QBL methods, further data are needed to understand the differences between EBL and QBL to enable accurate prediction of PPH.

KEY POINTS

· Machine learning methods may help predict PPH.. · EBL models do not generalize when QBL is used.. · Blood loss estimation alters model accuracy..

Simplified cardiovascular index may be the best comorbidity index for clinical use in prediction of mortality for renal cancer patients

OBJECTIVE

Understanding the relationship between comorbidities and life expectancy is important in cancer patients who carry risks of cancer and noncancer-related mortality. Comorbidity indices (CI) are tools to provide an objective measure of competing risks of death. We sought to determine which CI might be best incorporated into clinical practice for patients with suspected renal cancer.

MATERIALS AND METHODS

1572 patients diagnosed with renal masses (stage I-IV) between 1998 and 2016 were analyzed for this study. Patient data were gathered from a community-based health center. Comorbidities were evaluated individually, and with 1 of 4 CI: Charlson (CCI), updated CCI (uCCI), age-adjusted CCI (aCCI), and simplified cardiovascular index (CVI). Cox-proportional hazard analysis of all-cause mortality was performed using the four CI, adjusting for the 4 CI, adjusting for age, gender, race, tumor size, and tumor stage.

RESULTS

Univariable analyses revealed the four CI were significant predictors of mortality (P < 0.05), as were age, gender, tumor size, and stage. Comorbid conditions at diagnosis included hypertension (47.8%), diabetes mellitus (47.2%), coronary artery disease (41.1%), chronic kidney disease (31.8%), peripheral vascular disease (8.0%), congestive heart failure (5.7%), chronic obstructive pulmonary disease (5.7%), and cerebrovascular disease (2.0%). When analyzing the 4 CI in multivariable survival analyses accounting for factors available at diagnosis, and analyses incorporating pathologic and recurrence data, only CVI score and uCCI remained statistically significant (P < 0.05). Limitations of this work are the retrospective nature of data collection and data from a single institution, limiting the generalizability.

CONCLUSION

Increasing comorbidity, age, tumor size, and cM stage are predictors of ACM for suspected renal cancer patients. CVI appears to provide comparable information to various iterations of CCI (uCCI, aCCI) while being the simplest to use. Utilization of CVI may assist clinicians and patients when considering between interventional and noninterventional approaches for suspected renal cancer.

Recycling air conditioner-generated condensate water for microalgal biomass production and carbon dioxide sequestration

Air conditioners alleviate the discomfort of human beings from heat waves that are consequences of climate change caused by anthropogenic activities. With each passing year, the effects of global warming worsen, increasing the growth of air conditioning industry. Air conditioning units produce substantial amounts of non-nutritive and (generally) neglected condensate water and greenhouse gases. Considering this, the study explored the potential of using air conditioner condensate water (ACW) to cultivate Chlorella sorokiniana, producing biomass, and sequestering carbon dioxide (CO2). The maximum biomass production was obtained in the BG11 medium (1.45 g L-1), followed by ACW-50 (1.3 g L-1). Similarly, the highest chlorophyll-a content was observed in the BG11 medium (11 μg mL-1), followed by ACW-50 (9.11 μg mL-1). The ACW-50 cultures proved to be better adapted to physiological stress (Fv/Fm > 0.5) and can be suitable for achieving maximum biomass with adequate lipid, protein, and carbohydrate production. Moreover, C. sorokiniana demonstrated higher lipid and carbohydrate yields in the ACW-50 medium, while biomass production and protein yields were comparable to the BG11 medium. The lipid, protein, and carbohydrate productivity were 23.43, 32.9, and 23.19 mg L-1 d-1, respectively for ACW-50. Estimation of carbon capture potential through this approach equals to 9.5% of the total emissions which is an added advantage The results indicated that ACW could be effectively utilized for microalgae cultivation, reducing the reliance on freshwater for large-scale microalgal biomass production and reduce the carbon footprints of the air conditioning industry.

Digital health and acute kidney injury: consensus report of the 27th Acute Disease Quality Initiative workgroup

Acute kidney injury (AKI), which is a common complication of acute illnesses, affects the health of individuals in community, acute care and post-acute care settings. Although the recognition, prevention and management of AKI has advanced over the past decades, its incidence and related morbidity, mortality and health care burden remain overwhelming. The rapid growth of digital technologies has provided a new platform to improve patient care, and reports show demonstrable benefits in care processes and, in some instances, in patient outcomes. However, despite great progress, the potential benefits of using digital technology to manage AKI has not yet been fully explored or implemented in clinical practice. Digital health studies in AKI have shown variable evidence of benefits, and the digital divide means that access to digital technologies is not equitable. Upstream research and development costs, limited stakeholder participation and acceptance, and poor scalability of digital health solutions have hindered their widespread implementation and use. Here, we provide recommendations from the Acute Disease Quality Initiative consensus meeting, which involved experts in adult and paediatric nephrology, critical care, pharmacy and data science, at which the use of digital health for risk prediction, prevention, identification and management of AKI and its consequences was discussed.

Quantifying Clinicians' Diagnostic Uncertainty When Making Initial Treatment Decisions for Microbial Keratitis

PURPOSE

There is a need to understand physicians' diagnostic uncertainty in the initial management of microbial keratitis (MK). This study aimed to understand corneal specialists' diagnostic uncertainty by establishing risk thresholds for treatment of MK that could be used to inform a decision curve analysis for prediction modeling.

METHODS

A cross-sectional survey of corneal specialists with at least 2 years clinical experience was conducted. Clinicians provided the percentage risk at which they would always or never treat MK types (bacterial, fungal, herpetic, and amoebic) based on initial ulcer sizes and locations (<2 mm 2 central, <2 mm 2 peripheral, and >8 mm 2 central).

RESULTS

Seventy-two of 99 ophthalmologists participated who were 50% female with an average of 14.7 (SD = 10.1) years of experience, 60% in academic practices, and 38% outside the United States. Clinicians reported they would "never" and "always" treat a <2 mm 2 central MK infection if the median risk was 0% and 20% for bacterial (interquartile range, IQR = 0-5 and 5-50), 4.5% and 27.5% for herpetic (IQR = 0-10 and 10-50), 5% and 50% for fungal (IQR = 0-10 and 20-75), and 5% and 50.5% for amoebic (IQR = 0-20 and 32-80), respectively. Mixed-effects models showed lower thresholds to treat larger and central infections ( P < 0.001, respectively), and thresholds to always treat differed between MK types for the United States ( P < 0.001) but not international clinicians.

CONCLUSIONS

Risk thresholds to treat differed by practice locations and MK types, location, and size. Researchers can use these thresholds to understand when a clinician is uncertain and to create decision support tools to guide clinicians' treatment decisions.

Implications of the Use of Artificial Intelligence Predictive Models in Health Care Settings : A Simulation Study

BACKGROUND

Substantial effort has been directed toward demonstrating uses of predictive models in health care. However, implementation of these models into clinical practice may influence patient outcomes, which in turn are captured in electronic health record data. As a result, deployed models may affect the predictive ability of current and future models.

OBJECTIVE

To estimate changes in predictive model performance with use through 3 common scenarios: model retraining, sequentially implementing 1 model after another, and intervening in response to a model when 2 are simultaneously implemented.

DESIGN

Simulation of model implementation and use in critical care settings at various levels of intervention effectiveness and clinician adherence. Models were either trained or retrained after simulated implementation.

SETTING

Admissions to the intensive care unit (ICU) at Mount Sinai Health System (New York, New York) and Beth Israel Deaconess Medical Center (Boston, Massachusetts).

PATIENTS

130 000 critical care admissions across both health systems.

INTERVENTION

Across 3 scenarios, interventions were simulated at varying levels of clinician adherence and effectiveness.

MEASUREMENTS

Statistical measures of performance, including threshold-independent (area under the curve) and threshold-dependent measures.

RESULTS

At fixed 90% sensitivity, in scenario 1 a mortality prediction model lost 9% to 39% specificity after retraining once and in scenario 2 a mortality prediction model lost 8% to 15% specificity when created after the implementation of an acute kidney injury (AKI) prediction model; in scenario 3, models for AKI and mortality prediction implemented simultaneously, each led to reduced effective accuracy of the other by 1% to 28%.

LIMITATIONS

In real-world practice, the effectiveness of and adherence to model-based recommendations are rarely known in advance. Only binary classifiers for tabular ICU admissions data were simulated.

CONCLUSION

In simulated ICU settings, a universally effective model-updating approach for maintaining model performance does not seem to exist. Model use may have to be recorded to maintain viability of predictive modeling.

PRIMARY FUNDING SOURCE

National Center for Advancing Translational Sciences.

Oncogenic Activities of Tribbles1 (TRIB1) Pseudokinase Overexpressed in GBM are Mediated by Protein-Protein Interactions

PURPOSE/OBJECTIVE(S)

Glioblastoma (GBM) is the most aggressive form of glioma with a low 5-year survival rate. The current treatments are inadequate and crippled by therapy resistance. Therefore, there is an unmet need to identify druggable therapeutic targets in GBM. In this study we identified TRIB1, a Ser/Thr pseudokinase that acts as a scaffold to initiate Ubiquitin Proteasome System-mediated degradation of its substrates. We and others have found that TRIB1 activates the canonical MAPK and Akt signaling cascades. Previous reports also suggest that TRIB1 contributes to chemotherapy resistance in various cancers. Therefore, we evaluated oncogenic roles of TRIB1 in GBM cells and its contribution to therapy resistance.

MATERIALS/METHODS

Patient-centered reverse translational approach was utilized to identify novel therapeutic targets. To this end, TRIB1 was identified by statistical association (Cox regression analysis) of the patient-derived gene expression profiling data publicly available from TCGA GBM cohort. TRIB1 was functionally validated in vitro by generating stable overexpression cell lines (patient-derived) by antibiotic selection. Conditional knockdown of TRIB1 was achieved by doxycycline induction. Protein-protein interactions were evaluated by co-immunoprecipitation. Protein levels were detected by western blotting. Changes in tumor volume and overall survival (OS) were calculated.

RESULTS

The mRNA profiling of TCGA GBM cohort revealed that increased TRIB1 gene expression was associated with worse OS of GBM patients [HR = 1.3 (1.0-1.5); P = 0.019]. The same analyses in our institutional cohort revealed a similar association. Mice bearing TRIB1 transgene overexpressing tumors had the increased tumor volume and shorter OS compared to empty vector control at the end of experiment. Overexpression of TRIB1 increased the phosphorylation/activation of ERK and Akt in patient-derived primary cell lines. Akt but not ERK activation was decreased after TRIB1 knockdown. TRIB1 bound directly to ERK and Akt in these cells. TRIB1 also formed a complex with p53, COP1 and HDAC1 in patient-derived primary cell lines. This protein-protein interaction was independent of TP53 mutation status.

CONCLUSION

Our data suggest that TRIB1 overexpressed in GBM executes various oncogenic functions through interaction with different proteins. Activating ERK signaling, can induce cell proliferation. Similarly, by activating Akt it can cause prosurvival effects. Finally, by associating with HDAC1 and COP1, TRIB1 can modulate p53 function. All these protein-protein interactions ultimately contribute to chemoradiotherapy resistance in GBM cells. We are currently developing small molecule inhibitors targeting the above-mentioned interactions of TRIB1 to overcome therapeutic resistance.

Predicting persistent opioid use after hand surgery: a machine learning approach

BACKGROUND

The aim of this study was to evaluate the use of machine learning to predict persistent opioid use after hand surgery.

METHODS

We trained two algorithms to predict persistent opioid use, first using a general surgery dataset and then using a hand surgery dataset, resulting in four trained models. Next, we tested each model's performance using hand surgery data. Participants included adult surgery patients enrolled in a cohort study at an academic center from 2015-2018. The first algorithm (Michigan Genomics Initiative model) was designed to accommodate patient-reported data and patients with or without prior opioid use. The second algorithm (claims model) was designed for insurance claims data from patients who were opioid-naïve only. The main outcome was model discrimination, measured by area under the receiver operating curve (AUC).

RESULTS

Of 889 hand surgery patients, 49% were opioid-naïve and 21% developed persistent opioid use. Most patients underwent soft tissue procedures (55%) or fracture repair (20%). The MGI model had AUCs of 0.84 when trained only on hand surgery data, and 0.85 when trained on the full cohort of surgery patients. The claims model had AUCs of 0.69 when trained only on hand surgery data, and 0.52 when trained on the opioid-naïve cohort of surgery patients.

CONCLUSION

Opioid use is common after hand surgery. Machine learning has the potential to facilitate identification of patients who are at risk for prolonged opioid use, which can promote early interventions to prevent addiction.

Role of pinch in Argon impurity transport in ohmic discharges of Aditya-U Tokamak

We present experimental results of the trace argon impurity puffing in the ohmic plasmas of Aditya-U tokamak performed to study the argon transport behaviour. Argon line emissions in visible and Vacuum Ultra Violet (VUV) spectral ranges arising from the plasma edge and core respectively are measured simultaneously. During the experiments, space resolved brightness profile of Ar1+ line emissions at 472.69 nm (3p44s 2P3/2-3p44p 2D3/2), 473.59 nm (3p44s 4P5/2-3p44p 4P3/2), 476.49 nm (3p44s 2P1/2-3p44p 2P3/2), 480.60 nm (3p44s 4P5/2-3p44p 4P5/2) are recorded using a high resolution visible spectrometer. Also, a VUV spectrometer has been used to simultaneously observe Ar13+ line emission at 18.79 nm (2s22p 2P3/2-2s2p2 2P3/2) and Ar14+ line emission at 22.11 nm (2s2 1S0-2s2p 1P1). The diffusivity and convective velocity of Ar are obtained by comparing the measured radial emissivity profile of Ar1+ emission and the line intensity ratio of Ar13+ and Ar14+ ions, with those simulated using the impurity transport code, STRAHL. Argon diffusivities ~ 12 m2/s and ~ 0.3 m2/s have been observed in the edge (ρ > 0.85) and core region of the Aditya-U, respectively. The diffusivity values both in the edge and core region are found to be higher than the neo-classical values suggesting that the argon impurity transport is mainly anomalous in the Aditya-U tokamak. Also, an inward pinch of ~ 10 m/s mainly driven by Ware pinch is required to match the measured and simulated data. The measured peaked profile of Ar density suggests impurity accumulation in these discharges.

Universal DNA methylation age across mammalian tissues

Aging, often considered a result of random cellular damage, can be accurately estimated using DNA methylation profiles, the foundation of pan-tissue epigenetic clocks. Here, we demonstrate the development of universal pan-mammalian clocks, using 11,754 methylation arrays from our Mammalian Methylation Consortium, which encompass 59 tissue types across 185 mammalian species. These predictive models estimate mammalian tissue age with high accuracy (r > 0.96). Age deviations correlate with human mortality risk, mouse somatotropic axis mutations and caloric restriction. We identified specific cytosines with methylation levels that change with age across numerous species. These sites, highly enriched in polycomb repressive complex 2-binding locations, are near genes implicated in mammalian development, cancer, obesity and longevity. Our findings offer new evidence suggesting that aging is evolutionarily conserved and intertwined with developmental processes across all mammals.

Iron pill aspiration syndrome: A case report and literature review

Aspiration of iron pill containing ferrous sulfate into the airway can induce fulminant chemical burn and necrosis of the airway mucosa. Acute chemical burn and inflammatory response can result in life-threatening airway compromise. It can also result in long-term sequelae including but not limited to fibrosis and airway stenosis. Considering the common use of iron supplements, and the potential severity of aspiration related airway injury, clinicians should be fully cognizant of the interaction between aspirated iron and airway passages. Herein, we present a case report with pertinent review of the literature.

Social Risk Factor Associations With Presenting Visual Acuity in Patients With Microbial Keratitis

Importance

Neighborhood-level social risk factors may contribute to health disparities in microbial keratitis (MK) disease presentation. Understanding neighborhood-level factors may identify areas for revised health policies to address inequities that impact eye health.

Objective

To investigate if social risk factors were associated with presenting best-corrected visual acuity (BCVA) for patients with MK.

Design, Setting, and Participants

This was a cross-sectional study of patients with a diagnosis of MK. Patients presenting to the University of Michigan with a diagnosis of MK between August 1, 2012, and February 28, 2021, were included in the study. Patient data were obtained from the University of Michigan electronic health record.

Main Outcomes and Measures

Individual-level characteristics (age, self-reported sex, self-reported race and ethnicity), presenting log of the minimum angle of resolution (logMAR) BCVA, and neighborhood-level factors, including measures on deprivation, inequity, housing burden, and transportation at the census block group, were obtained. Univariate associations of presenting BCVA (< 20/40 vs ≥20/40) with individual-level characteristics were assessed with 2-sample t, Wilcoxon, and χ2 tests. Logistic regression was used to test associations of neighborhood-level characteristics with the probability of presenting BCVA worse than 20/40 after adjustment for patient demographics.

Results

A total of 2990 patients with MK were identified and included in the study. Patients had a mean (SD) age of 48.6 (21.3) years, and 1723 were female (57.6%). Patients self-identified with the following race and ethnicity categories: 132 Asian (4.5%), 228 Black (7.8%), 99 Hispanic (3.5%), 2763 non-Hispanic (96.5%), 2463 White (84.4%), and 95 other (3.3%; included any race not previously listed). Presenting BCVA had a median (IQR) value of 0.40 (0.10-1.48) logMAR units (Snellen equivalent, 20/50 [20/25-20/600]), and 1508 of 2798 patients (53.9%) presented with BCVA worse than 20/40. Patients presenting with logMAR BCVA less than 20/40 were older than those who presented with 20/40 or higher (mean difference, 14.7 years; 95% CI, 13.3-16.1; P < .001). Furthermore, a larger percentage of male vs female sex patients presented with logMAR BCVA less than 20/40 (difference, 5.2%; 95% CI, 1.5-8.9; P = .04), as well as Black race (difference, 25.7%; 95% CI, 15.0%-36.5%;P < .001) and White race (difference, 22.6%; 95% CI, 13.9%-31.3%; P < .001) vs Asian race, and non-Hispanic vs Hispanic ethnicity (difference, 14.6%; 95% CI, 4.5%-24.8%; P = .04). After adjusting for age, self-reported sex, and self-reported race and ethnicity, worse Area Deprivation Index (odds ratio [OR], 1.30 per 10-unit increase; 95% CI, 1.25-1.35; P < .001), increased segregation (OR, 1.44 per 0.1-unit increase in Theil H index; 95% CI, 1.30-1.61; P < .001), higher percentage of households with no car (OR, 1.25 per 1 percentage point increase; 95% CI, 1.12-1.40; P = .001), and lower average number of cars per household (OR, 1.56 per 1 less car; 95% CI, 1.21-2.02; P = .003) were associated with increased odds of presenting BCVA worse than 20/40.

Conclusion and Relevance

Findings of this cross-sectional study suggest that in a sample of patients with MK, patient characteristics and where they live were associated with disease severity at presentation. These findings may inform future research on social risk factors and patients with MK.

Cytomorphological Spectrum of Head and Neck Lesions by Fine Needle Aspiration Cytology in a Tertiary Care Center

Introduction

Fine needle aspiration cytology (FNAC) is considered the first line investigation of choice for evaluating head and neck swellings as it is a quick, safe, and rapid diagnostic procedure.

Material and Methods

This is a retrospective study that included 242 cases of head and neck lesions in the Department of Pathology, Maharaja Agrasen Medical College, Agroha. FNAC was performed by aspiration and non aspiration techniques, and cytological diagnosis was given and correlated with clinical findings and investigations.

Results

The most common age group affected was 21-30 years. Male to female ratio was 1:1.49. Out of 242 cases, maximum lesions were found in lymph nodes (128 cases), followed by thyroid gland in 81 cases, salivary gland in 23 cases, and miscellaneous group in 10 cases. Maximum number of cases reported were inflammatory (55.37%), followed by benign (29.33%) and malignant (11.15%) cases. Most swellings occurring in the head and neck region are inflammatory in nature.

Conclusion

Our study concluded that FNAC is a simple, safe, and minimal invasive technique that differentiates between neoplastic and non neoplastic lesions and avoids unnecessary surgeries.

Development and Validation of a Model to Predict Ureteral Stent Placement Following Ureteroscopy: Results From a Statewide Collaborative

OBJECTIVE

To develop and validate a model to predict whether patients undergoing ureteroscopy (URS) will receive a stent.

METHODS

Using registry data obtained from the Michigan Urological Surgery Improvement Collaborative Reducing Operative Complications from Kidney Stones initiative, we identified patients undergoing URS from 2016 to 2020. We used patients' age, sex, body mass index, size and location of the largest stone, current stent in place, history of any kidney stone procedure, procedure type, and acuity to fit a multivariable logistic regression model to a derivation cohort consisting of a random two-thirds of episodes. Model discrimination and calibration were evaluated in the validation cohort. A sensitivity analysis examined urologist variation using generalized mixed-effect models.

RESULTS

We identified 15,048 URS procedures, of which 11,471 (76%) had ureteral stents placed. Older age, male sex, larger stone size, the largest stone being in the ureteropelvic junction, no prior stone surgery, no stent in place, a planned procedure type of laser lithotripsy, and urgent procedure were associated with a higher risk of stent placement. The model achieved an area under the receiver operating characteristic curve of 0.69 (95% CI 0.67, 0.71). Incorporating urologist-level variation improved the area under the receiver operating characteristic curve to 0.83 (95% CI 0.82, 0.84).

CONCLUSION

Using a large clinical registry, we developed a multivariable regression model to predict ureteral stent placement following URS. Though well-calibrated, the model had modest discrimination due to heterogeneity in practice patterns in stent placement across urologists.

In vitro comparative evaluation of physical and chemical properties of surface enamel after using APF and SDF with or without laser activation

PURPOSE

To evaluate and compare the changes in physical and chemical properties of the enamel surface after application of Silver Diamine Fluoride (SDF), Acidulated Phosphate Fluoride (APF), LASER activated SDF and LASER activated APF.

METHODS

Sample consisted of 72 freshly extracted healthy human premolar teeth, extracted for orthodontic purpose, free from caries, fracture or any anomalies. The selected samples were randomly divided into four groups (n = 18): Group 1 (SDF); Group 2 (APF); Group 3 (LASER activated SDF) and Group 4 (LASER activated APF). All samples were evaluated for values of DIAGNOdent at baseline, after demineralisation and after remineralisation. They were further divided and assessed for colour changes, surface alterations and fluoride content of surface enamel using Spectrophotometer, Scanning Electron Microscopy and Energy Dispersive X-ray Spectrometry respectively. The statistical analysis was done using One-Way ANOVA, Tukey's HSD test, Mann-Whitney U test and Kruskal-Wallis Test.

RESULTS

Highest remineralising potential and maximum colour changes of surface enamel was exhibited by Group 3. Scanning Electron Micrographs of Group 3 and Group 4 exhibited regular globular structures of enamel whereas Group 1 and Group 2 showed irregular globular surface of the enamel at 2000× and 5000× magnification. Maximum fluoride uptake on the surface enamel was seen in Group 4 followed by Group 3.

CONCLUSION

Use of LASER activated topical fluorides helps to achieve superior caries prevention. LASER activated APF can be used as an aesthetic alternative to SDF, as LASER activated APF showed higher uptake of fluoride on the enamel surface without discoloration effect.

Adult Brainstem Astroblastoma: Unusual Presentation of a Rare Tumor

Astroblastoma is a rare tumor, which is mostly found in pediatric population. Due to scarcity of literature, the data about treatment is lacking. We are reporting case of brainstem astroblastoma in an adult female. A 45-year-old lady presented with complaint of headache, vertigo, vomiting, and nasal regurgitation for 3 months. On examination, she had weak gag, left hemiparesis. Magnetic resonance imaging brain reported medulla oblongata mass, dorsally exophytic. She underwent suboccipital craniotomy and decompression of mass. Histopathology confirmed diagnosis of astroblastoma. She underwent radiotherapy and recovered well. Brainstem astroblastoma is an extremely rare entity. The surgical resection is possible due to well-defined plane. For best outcome, maximum resection and radiation are indicated.

Factors Associated With Variability in the Performance of a Proprietary Sepsis Prediction Model Across 9 Networked Hospitals in the US

This cohort study uses data from electronic health records to assess variability in a sepsis prediction model across 9 hospitals.

External Validation and Comparison of a General Ward Deterioration Index Between Diversely Different Health Systems

OBJECTIVES

Implementing a predictive analytic model in a new clinical environment is fraught with challenges. Dataset shifts such as differences in clinical practice, new data acquisition devices, or changes in the electronic health record (EHR) implementation mean that the input data seen by a model can differ significantly from the data it was trained on. Validating models at multiple institutions is therefore critical. Here, using retrospective data, we demonstrate how Predicting Intensive Care Transfers and other UnfoReseen Events (PICTURE), a deterioration index developed at a single academic medical center, generalizes to a second institution with significantly different patient population.

DESIGN

PICTURE is a deterioration index designed for the general ward, which uses structured EHR data such as laboratory values and vital signs.

SETTING

The general wards of two large hospitals, one an academic medical center and the other a community hospital.

SUBJECTS

The model has previously been trained and validated on a cohort of 165,018 general ward encounters from a large academic medical center. Here, we apply this model to 11,083 encounters from a separate community hospital.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

The hospitals were found to have significant differences in missingness rates (> 5% difference in 9/52 features), deterioration rate (4.5% vs 2.5%), and racial makeup (20% non-White vs 49% non-White). Despite these differences, PICTURE's performance was consistent (area under the receiver operating characteristic curve [AUROC], 0.870; 95% CI, 0.861-0.878), area under the precision-recall curve (AUPRC, 0.298; 95% CI, 0.275-0.320) at the first hospital; AUROC 0.875 (0.851-0.902), AUPRC 0.339 (0.281-0.398) at the second. AUPRC was standardized to a 2.5% event rate. PICTURE also outperformed both the Epic Deterioration Index and the National Early Warning Score at both institutions.

CONCLUSIONS

Important differences were observed between the two institutions, including data availability and demographic makeup. PICTURE was able to identify general ward patients at risk of deterioration at both hospitals with consistent performance (AUROC and AUPRC) and compared favorably to existing metrics.

Mid-circuit correction of correlated phase errors using an array of spectator qubits

Scaling up invariably error-prone quantum processors is a formidable challenge. Although quantum error correction ultimately promises fault-tolerant operation, the required qubit overhead and error thresholds are daunting. In a complementary proposal, co-located, auxiliary 'spectator' qubits act as in-situ probes of noise, and enable real-time, coherent corrections of data qubit errors. We use an array of cesium spectator qubits to correct correlated phase errors on an array of rubidium data qubits. By combining in-sequence readout, data processing, and feed-forward operations, these correlated errors are suppressed within the execution of the quantum circuit. The protocol is broadly applicable to quantum information platforms, and establishes key tools for scaling neutral-atom quantum processors: mid-circuit readout of atom arrays, real-time processing and feed-forward, and coherent mid-circuit reloading of atomic qubits.

Extended minimal enteral feeding and time to regain birth weight in extremely low-birth-weight infants

BACKGROUND

Minimal enteral feeding after birth has been developed as a strategy to enhance the functional maturation of the gastrointestinal tract. This study aimed to examine the relationship between the duration of minimal enteral feeding and time to regain birth weight in extremely low-birth-weight infants.

METHODS

This retrospective study included all extremely low-birth-weight infants born between January 2018 and December 2020. Infants with major congenital anomalies and conditions requiring surgery and those who died or received palliative care in the first 10 days of life were excluded from the analysis. Minimal enteral feeding courses were categorized as extended if the feeding was continued for >  72 hours and short if the feeding was <  72 hours. The primary measured outcome was the time taken to regain birth weight.

RESULTS

Of 217 study infants, 180 received an extended minimal enteral feeding for >  72 h. The median time to regain birth weight was not significantly different between the extended and short minimal enteral feeding groups, median (IQR) was 10 (7-13) versus 8 (6-11), respectively (p = 0.15). Extended minimal enteral feeding is associated with a significant increase in the mean duration of the total parenteral nutrition, (21.3±10 versus 17.2±9.3 days; p = 0.021). Infants with prolonged minimal enteral feeding courses experienced non-significantly higher levels of necrotizing enterocolitis, late-onset sepsis, and retinopathy of prematurity.

CONCLUSIONS

Extended minimal enteral feeding in extremely low-birth-weight infants may not affect the time taken to regain birth weight.

Aberrant right subclavian artery: a case of vertebrobasilar insufficiency

Aberrant right subclavian artery (ARSA) is a rare congenital malformation, wherein the aorta gives rise to the right subclavian artery as a branch off the aortic arch distal to the takeoff of the left subclavian artery. We presented a case of a patient with ARSA that manifested vertebrobasilar symptoms. PubMed search was preformed using keywords 'aberrant right subclavian artery', 'right subclavian steal' and 'vertebrobasilar', which generated nine articles. We found only seven case reports through a PubMed search that discuss ARSA in association with Subclavian steal syndrome. Approximately 71% (n = 5) of the patients in our literature review manifested with signs and symptoms of vertebrobasilar insufficiency. Given the complex anatomy in this condition, treatment should be aimed at symptom resolution. Carotid-subclavian bypass ultimately resolved the symptoms in our patient. Management in symptomatic patient is surgical. In addition to open technique, endovascular interventions can be an option.

Leptomeningeal disease as a presenting feature of gestational trophoblastic neoplasia: A review and recommendations for management

OBJECTIVES

Gestational Trophoblastic Neoplasia (GTN) is a rare group of malignant placental-related tumours requiring systemic anti-cancer treatment. Leptomeningeal disease (LMD) related to GTN is not well reported with no consensus in optimal treatment. We offer recommendations for management of these patients.

METHODS

We discuss five patients with GTN who presented with features of LMD and were diagnosed with gadolinium-enhanced MRI brain, all of whom received low dose induction etoposide-cisplatin (EP) followed by either EP-etoposide, methotrexate (CNS) and actinomycin-D (EMA) or EMA(CNS)-cyclophosphamide and vincristine (CO).

RESULTS

Four out of the five patients additionally received intrathecal methotrexate. Four patients had complete hCG response to first line multi-agent chemotherapy, one patient required second line paclitaxel, cisplatin alternating with paclitaxel, etoposide (TP/TE), where paclitaxel was substituted with nab-paclitaxel due to anaphylaxis, followed by hysterectomy. One of the four initial complete hCG responders relapsed in the lung requiring further systemic treatment with subsequent lobectomy. Patient reported outcomes indicate persistent neurological symptoms are mild and do not affect functionality and quality of life.

CONCLUSION

With a follow-up range of 2-6 years, all five patients remain cured demonstrating excellent survival outcomes with the avoidance of whole-brain radiotherapy in all cases.

Assessing the net benefit of machine learning models in the presence of resource constraints

OBJECTIVE

The objective of this study is to provide a method to calculate model performance measures in the presence of resource constraints, with a focus on net benefit (NB).

MATERIALS AND METHODS

To quantify a model's clinical utility, the Equator Network's TRIPOD guidelines recommend the calculation of the NB, which reflects whether the benefits conferred by intervening on true positives outweigh the harms conferred by intervening on false positives. We refer to the NB achievable in the presence of resource constraints as the realized net benefit (RNB), and provide formulae for calculating the RNB.

RESULTS

Using 4 case studies, we demonstrate the degree to which an absolute constraint (eg, only 3 available intensive care unit [ICU] beds) diminishes the RNB of a hypothetical ICU admission model. We show how the introduction of a relative constraint (eg, surgical beds that can be converted to ICU beds for very high-risk patients) allows us to recoup some of the RNB but with a higher penalty for false positives.

DISCUSSION

RNB can be calculated in silico before the model's output is used to guide care. Accounting for the constraint changes the optimal strategy for ICU bed allocation.

CONCLUSIONS

This study provides a method to account for resource constraints when planning model-based interventions, either to avoid implementations where constraints are expected to play a larger role or to design more creative solutions (eg, converted ICU beds) to overcome absolute constraints when possible.

Prediction of Visual Acuity in Patients With Microbial Keratitis

PURPOSE

The purpose of this study was to predict visual acuity (VA) 90 days after presentation for patients with microbial keratitis (MK) from data at the initial clinical ophthalmic encounter.

METHODS

Patients with MK were identified in the electronic health record between August 2012 and February 2021. Random forest (RF) models were used to predict 90-day VA < 20/40 [visual impairment (VI)]. Predictors evaluated included age, sex, initial VA, and information documented in notes at presentation. Model diagnostics are reported with 95% confidence intervals (CIs) for area under the curve (AUC), misclassification rate, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).

RESULTS

One thousand seven hundred ninety-one patients were identified. The presenting logMAR VA was on average 0.86 (Snellen equivalent and standard deviation = 20/144 ± 12.6 lines) in the affected or worse eye, and 43.6% with VI. VI at 90-day follow-up was present in the affected eye or worse eye for 26.9% of patients. The RF model for predicting 90-day VI had an AUC of 95% (CI: 93%-97%) and a misclassification rate of 9% (7%-12%). The percent sensitivity, specificity, PPV, and NPV were 86% (80%-91%), 92% (89%-95%), 81% (74%-86%), and 95% (92%-97%), respectively. Older age, worse presenting VA, and more mentions of "penetrating keratoplasty" and "bandage contact lens" were associated with increased probability of 90-day VI, whereas more mentions of "quiet" were associated with decreased probability of 90-day VI.

CONCLUSIONS

RF modeling yielded good sensitivity and specificity to predict VI at 90 days which could guide clinicians about the risk of poor vision outcomes for patients with MK.

Generalizability of an acute kidney injury prediction model across health systems

Delays in the identification of acute kidney injury (AKI) in hospitalized patients are a major barrier to the development of effective interventions to treat AKI. A recent study by Tomasev and colleagues at DeepMind described a model that achieved a state-of-the-art performance in predicting AKI up to 48 hours in advance.1 Because this model was trained in a population of US Veterans that was 94% male, questions have arisen about its reproducibility and generalizability. In this study, we aimed to reproduce key aspects of this model, trained and evaluated it in a similar population of US Veterans, and evaluated its generalizability in a large academic hospital setting. We found that the model performed worse in predicting AKI in females in both populations, with miscalibration in lower stages of AKI and worse discrimination (a lower area under the curve) in higher stages of AKI. We demonstrate that while this discrepancy in performance can be largely corrected in non-Veterans by updating the original model using data from a sex-balanced academic hospital cohort, the worse model performance persists in Veterans. Our study sheds light on the importance of reproducing artificial intelligence studies, and on the complexity of discrepancies in model performance in subgroups that cannot be explained simply on the basis of sample size.

Seasonal variation in diagnosis of cutaneous invasive melanoma and cutaneous squamous cell carcinoma: A nationwide study in the Netherlands

BACKGROUND

Currently, there is no study that has reported on the seasonal trends of skin cancer in the Netherlands. This study aimed to investigate seasonal variation in diagnosis of cutaneous melanoma (CM) and cutaneous squamous cell carcinoma (cSCC) focusing on different subgroups.

METHODS

CM diagnosed from 2001 till 2019 and cSCCs from 2001 till 2015 were selected from the Netherlands Cancer Registry. The monthly distribution of CM and cSCC diagnoses were evaluated. Summer-to-winter ratios (SWRs) were calculated overall and stratified by patient and tumour characteristics.

RESULTS

Significant increases in melanoma incidence were noted over the summer months (SWR 1.39 (CI 1.37-1.40)). This increase was less apparent for cSCCs, as higher incidence rates were observed in the months September-November (SWR 1.13 (CI 1.12-1.14)). The seasonal variation of CM was greater in women and younger people, in superficial spreading melanoma and lentigo maligna melanoma, for the extremities, in thinner lesions, and for stage I at diagnosis. The seasonal variation of cSCC was similar for both sexes, most marked in patients 45-69 and ≥ 70, and for the extremities.

CONCLUSIONS

Our findings showed a pronounced seasonal variation in the diagnosis of CM with a peak in the summer months. For cSCC, no evident peak was observed, but an increase in diagnosis was noted in fall. Both CM and cSCC showed strong seasonal effects for the extremities.

Undernutrition and associated factors among lactating mothers in Dehradun, Uttarakhand, India

Undernutrition was one of the most widespread public health problems that affected both developed and developing countries. In India, it was one of the factors which lead to unacceptably high morbidity and mortality among women. However, little was documented on undernutrition among lactating women, particularly in the urban community. This study aimed to evaluate the nutritional status and its related factors among lactating mothers in the urban areas of the Dehradun region, Uttarakhand, India. A structured, pre-tested, and validated questionnaire was used to capture the sociodemographic information including the economical and medical conditions of 150 lactating women in Dehradun, India. Pearson correlation coefficient and association of various factors determined that 31.33% of women were in the age bracket of 20-25 years and the low-income group (72%). The prevalence of underweight was 7.33%, and the mean and standard deviation of the body mass index of mothers were 20.59±2.96 and 21.70±3.18 for sedentary and moderate workers respectively. There were significant correlations found between BMI, energy, carbohydrates, and fat intake (p<0.05). A multivariate regression model was used to associate the nutritional status of the participant’s income group, education, type of work, age of mothers, and frequency of meals. Based on the results, intervention programs for dietary correction and the effect of nutrition on the body were emphasized to lactating mothers for better health and nutritional outcomes.

ECG abnormalities and their relation to COVID-19 outcomes – a WHF study

Introduction

COVID-19 is a respiratory tract infection caused by the Coronavirus (SARS-CoV-2) and its main clinical manifestations are respiratory. The cardiovascular system can also be affected, especially in patients with severe acute respiratory syndrome [1].

On the other hand, cardiovascular disease (CVD) and risk factors have been shown to be predictors of poor outcomes in COVID-19 [2]. Diverse electrocardiographic abnormalities can be found in this condition [3], although their value as a prognostic predictor have not been properly established due to heterogeneity in abnormalities evaluation and small sample sizes in related studies [4].

Purpose

The aim of the present study is to evaluate the association of electrocardiogram (ECG) findings to poor COVID-19 outcomes

Methods

This is a multicentric cohort study that followed hospitalized adults due to COVID-19, from low-middle and high-income countries as part of the World Heart Federation (WHF) Global Study on CVD and COVID-19 initiative [5]. Participants were followed up from hospital admission until 30 days post discharge. For the present study, participants with a valid ECG were included. ECG findings were described according to standardized measurements [heart rate, PR interval, QRS duration and axis, corrected QT interval (QTc)] and abnormalities (according to the Minnesota code system). Abnormalities utilized were grouped into ischemic abnormalities (q waves and ST-T abnormalities), atrial fibrillation (AF), prolonged QTc, sinus tachycardia (defined for the study as above 120 bpm), right and left bundle branch block and presence of any major abnormality. The primary outcome was defined as death from any cause. The secondary outcomes were intensive care unit (ICU) admission and cardiovascular events (myocarditis, pericarditis, myocardial infarction, acute heart failure, ischemic and hemorrhagic stroke). Multiple logistic regression was used to evaluate the association of ECG abnormalities to the outcomes of interest. Adjustments were made in a step by step fashion including gender, age, country of residence, cardiovascular risk factors (diabetes, hypertension, tobacco use) and presence of comorbidities (CVD, asthma, cancer, immunosuppression and chronic kidney disease).

Results

The clinical characteristics of the cohort are described in table 1. Figure 1 represents the odds ratio and its 95% confidence interval of having the defined outcomes when presenting a ECG abnormality for the final regression model.

Conclusion

ECG abnormalities were independently related to poor outcomes in COVID-19 after accounting for multiple confounders. Significant associations were more frequently found for ischemic abnormalities, heart rate above 120 bpm, atrial fibrillation and having at least one major electrocardiographic abnormality.

Funding Acknowledgement

Type of funding sources: Other. Main funding source(s): Pfizer and Sanofi PasteurWorld Heart Federation

Lessons in machine learning model deployment learned from sepsis

In three recent and related publications, researchers from Johns Hopkins University and Bayesian Health report results from implementing and prospectively evaluating the Targeted Real-time Early Warning System (TREWS) for sepsis at five hospitals.^1-3.

Evaluation of prognostic risk models for postoperative pulmonary complications in adult patients undergoing major abdominal surgery: a systematic review and international external validation cohort study

BACKGROUND

Stratifying risk of postoperative pulmonary complications after major abdominal surgery allows clinicians to modify risk through targeted interventions and enhanced monitoring. In this study, we aimed to identify and validate prognostic models against a new consensus definition of postoperative pulmonary complications.

METHODS

We did a systematic review and international external validation cohort study. The systematic review was done in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We searched MEDLINE and Embase on March 1, 2020, for articles published in English that reported on risk prediction models for postoperative pulmonary complications following abdominal surgery. External validation of existing models was done within a prospective international cohort study of adult patients (≥18 years) undergoing major abdominal surgery. Data were collected between Jan 1, 2019, and April 30, 2019, in the UK, Ireland, and Australia. Discriminative ability and prognostic accuracy summary statistics were compared between models for the 30-day postoperative pulmonary complication rate as defined by the Standardised Endpoints in Perioperative Medicine Core Outcome Measures in Perioperative and Anaesthetic Care (StEP-COMPAC). Model performance was compared using the area under the receiver operating characteristic curve (AUROCC).

FINDINGS

In total, we identified 2903 records from our literature search; of which, 2514 (86·6%) unique records were screened, 121 (4·8%) of 2514 full texts were assessed for eligibility, and 29 unique prognostic models were identified. Nine (31·0%) of 29 models had score development reported only, 19 (65·5%) had undergone internal validation, and only four (13·8%) had been externally validated. Data to validate six eligible models were collected in the international external validation cohort study. Data from 11 591 patients were available, with an overall postoperative pulmonary complication rate of 7·8% (n=903). None of the six models showed good discrimination (defined as AUROCC ≥0·70) for identifying postoperative pulmonary complications, with the Assess Respiratory Risk in Surgical Patients in Catalonia score showing the best discrimination (AUROCC 0·700 [95% CI 0·683-0·717]).

INTERPRETATION

In the pre-COVID-19 pandemic data, variability in the risk of pulmonary complications (StEP-COMPAC definition) following major abdominal surgery was poorly described by existing prognostication tools. To improve surgical safety during the COVID-19 pandemic recovery and beyond, novel risk stratification tools are required.

FUNDING

British Journal of Surgery Society.

Automated Determination of Left Ventricular Function Using Electrocardiogram Data in Patients on Maintenance Hemodialysis

BACKGROUND AND OBJECTIVES

Left ventricular ejection fraction is disrupted in patients on maintenance hemodialysis and can be estimated using deep learning models on electrocardiograms. Smaller sample sizes within this population may be mitigated using transfer learning.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

We identified patients on hemodialysis with transthoracic echocardiograms within 7 days of electrocardiogram using diagnostic/procedure codes. We developed four models: (1) trained from scratch in patients on hemodialysis, (2) pretrained on a publicly available set of natural images (ImageNet), (3) pretrained on all patients not on hemodialysis, and (4) pretrained on patients not on hemodialysis and fine-tuned on patients on hemodialysis. We assessed the ability of the models to classify left ventricular ejection fraction into clinically relevant categories of ≤40%, 41% to ≤50%, and >50%. We compared performance by area under the receiver operating characteristic curve.

RESULTS

We extracted 705,075 electrocardiogram:echocardiogram pairs for 158,840 patients not on hemodialysis used for development of models 3 and 4 and n=18,626 electrocardiogram:echocardiogram pairs for 2168 patients on hemodialysis for models 1, 2, and 4. The transfer learning model achieved area under the receiver operating characteristic curves of 0.86, 0.63, and 0.83 in predicting left ventricular ejection fraction categories of ≤40% (n=461), 41%-50% (n=398), and >50% (n=1309), respectively. For the same tasks, model 1 achieved area under the receiver operating characteristic curves of 0.74, 0.55, and 0.71, respectively; model 2 achieved area under the receiver operating characteristic curves of 0.71, 0.55, and 0.69, respectively, and model 3 achieved area under the receiver operating characteristic curves of 0.80, 0.51, and 0.77, respectively. We found that predictions of left ventricular ejection fraction by the transfer learning model were associated with mortality in a Cox regression with an adjusted hazard ratio of 1.29 (95% confidence interval, 1.04 to 1.59).

CONCLUSION

A deep learning model can determine left ventricular ejection fraction for patients on hemodialysis following pretraining on electrocardiograms of patients not on hemodialysis. Predictions of low ejection fraction from this model were associated with mortality over a 5-year follow-up period.

PODCAST

This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2022_06_06_CJN16481221.mp3.