Practical applications of three-dimensional printing for process improvement in the cytopathology laboratory.

With the increased availability of three-dimensional (3D) printers, innovative teaching and training materials have been created in medical fields. For pathology, the use of 3D printing has been largely limited to anatomic representations of disease processes or the development of supplies during the coronavirus disease 2019 pandemic. Herein, an institution's 3D printing laboratory and staff with expertise in additive manufacturing illustrate how this can address design issues in cytopathology specimen collection and processing. The authors' institutional 3D printing laboratory, along with students and trainees, used computer-aided design and 3D printers to iterate on design, create prototypes, and generate final usable materials using additive manufacturing. The program Microsoft Forms was used to solicit qualitative and quantitative feedback. The 3D-printed models were created to assist with cytopreparation, rapid on-site evaluation, and storage of materials in the preanalytical phase of processing. These parts provided better organization of materials for cytology specimen collection and staining, in addition to optimizing storage of specimens with multiple sized containers to optimize patient safety. The apparatus also allowed liquids to be stabilized in transport and removed faster at the time of rapid on-site evaluation. Rectangular boxes were also created to optimally organize all components of a specimen in cytopreparation to simplify and expedite the processes of accessioning and processing, which can minimize errors. These practical applications of 3D printing in the cytopathology laboratory demonstrate the utility of the design and printing process on improving aspects of the workflow in cytopathology laboratories to maximize efficiency, organization, and patient safety.

Patient Perspectives on Artificial Intelligence in Healthcare Decision Making: A Multi-Center Comparative Study.

Objective: Investigate the patient opinion on the use of Artificial Intelligence (AI) in Orthopaedics. Methods: 397 orthopaedic patients from a large urban academic center and a rural health system completed a 37-component survey querying patient demographics and perspectives on clinical scenarios involving AI. An average comfort score was calculated from thirteen Likert-scale questions (1, not comfortable; 10, very comfortable). Secondary outcomes requested a binary opinion on whether it is acceptable for patient healthcare data to be used to create AI (yes/no) and the impact of AI on: orthopaedic care (positive/negative); healthcare cost (increase/decrease); and their decision to refuse healthcare if cost increased (yes/no). Bivariate and multivariable analyses were employed to identify characteristics that impacted patient perspectives. Results: The average comfort score across the population was 6.4, with significant bivariate differences between age (p = 0.0086), gender (p = 0.0001), education (p = 0.0029), experience with AI/ML (p < 0.0001), survey format (p < 0.0001), and four binary outcomes (p < 0.05). When controlling for age and education, multivariable regression identified significant relationships between comfort score and experience with AI/ML (p = 0.0018) and each of the four binary outcomes (p < 0.05). In the final multivariable model gender, survey format, perceived impact of AI on orthopaedic care, and the decision to refuse care if it were to increase cost remained significantly associated with the average AI comfort score (p < 0.05). Additionally, patients were not comfortable undergoing surgery entirely by a robot with distant physician supervision compared to close supervision. Conclusion: The orthopaedic patient appears comfortable with AI joining the care team.

The State of Machine Learning in Spine Surgery: A Systematic Review.

STUDY DESIGN: This was a systematic review of existing literature. OBJECTIVE: The objective of this study was to evaluate the current state-of-the-art trends and utilization of machine learning in the field of spine surgery. SUMMARY OF BACKGROUND DATA: The past decade has seen a rise in the clinical use of machine learning in many fields including diagnostic radiology and oncology. While studies have been performed that specifically pertain to spinal surgery, there have been relatively few aggregate reviews of the existing scientific literature as applied to clinical spine surgery. METHODS: This study utilized Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to review the scientific literature from 2009 to 2019 with syntax specific for machine learning and spine surgery applications. Specific data was extracted from the available literature including algorithm application, algorithms tested, database type and size, algorithm training method, and outcome of interest. RESULTS: A total of 44 studies met inclusion criteria, of which the majority were level III evidence. Studies were grouped into 4 general types: diagnostic tools, clinical outcome prediction, surgical assessment tools, and decision support tools. Across studies, a wide swath of algorithms were used, which were trained across multiple disparate databases. There were no studies identified that assessed the ethical implementation or patient perceptions of machine learning in clinical care. CONCLUSIONS: The results reveal the broad range of clinical applications and methods used to create machine learning algorithms for use in the field of spine surgery. Notable disparities exist in algorithm choice, database characteristics, and training methods. Ongoing research is needed to make machine learning operational on a large scale.

Deep-learning-assisted analysis of echocardiographic videos improves predictions of all-cause mortality.

Machine learning promises to assist physicians with predictions of mortality and of other future clinical events by learning complex patterns from historical data, such as longitudinal electronic health records. Here we show that a convolutional neural network trained on raw pixel data in 812,278 echocardiographic videos from 34,362 individuals provides superior predictions of one-year all-cause mortality. The model's predictions outperformed the widely used pooled cohort equations, the Seattle Heart Failure score (measured in an independent dataset of 2,404 patients with heart failure who underwent 3,384 echocardiograms), and a machine learning model involving 58 human-derived variables from echocardiograms and 100 clinical variables derived from electronic health records. We also show that cardiologists assisted by the model substantially improved the sensitivity of their predictions of one-year all-cause mortality by 13% while maintaining prediction specificity. Large unstructured datasets may enable deep learning to improve a wide range of clinical prediction models.

Computational simulation to assess patient safety of uncompensated COVID-19 two-patient ventilator sharing using the Pulse Physiology Engine.

BACKGROUND: The COVID-19 pandemic is stretching medical resources internationally, sometimes creating ventilator shortages that complicate clinical and ethical situations. The possibility of needing to ventilate multiple patients with a single ventilator raises patient health and safety concerns in addition to clinical conditions needing treatment. Wherever ventilators are employed, additional tubing and splitting adaptors may be available. Adjustable flow-compensating resistance for differences in lung compliance on individual limbs may not be readily implementable. By exploring a number and range of possible contributing factors using computational simulation without risk of patient harm, this paper attempts to define useful bounds for ventilation parameters when compensatory resistance in limbs of a shared breathing circuit is not possible. This desperate approach to shared ventilation support would be a last resort when alternatives have been exhausted. METHODS: A whole-body computational physiology model (using lumped parameters) was used to simulate each patient being ventilated. The primary model of a single patient with a dedicated ventilator was augmented to model two patients sharing a single ventilator. In addition to lung mechanics or estimation of CO2 and pH expected for set ventilation parameters (considerations of lung physiology alone), full physiological simulation provides estimates of additional values for oxyhemoglobin saturation, arterial oxygen tension, and other patient parameters. A range of ventilator settings and patient characteristics were simulated for paired patients. FINDINGS: To be useful for clinicians, attention has been directed to clinically available parameters. These simulations show patient outcome during multi-patient ventilation is most closely correlated to lung compliance, oxygenation index, oxygen saturation index, and end-tidal carbon dioxide of individual patients. The simulated patient outcome metrics were satisfactory when the lung compliance difference between two patients was less than 12 mL/cmH2O, and the oxygen saturation index difference was less than 2 mmHg. INTERPRETATION: In resource-limited regions of the world, the COVID-19 pandemic will result in equipment shortages. While single-patient ventilation is preferable, if that option is unavailable and ventilator sharing using limbs without flow resistance compensation is the only available alternative, these simulations provide a conceptual framework and guidelines for clinical patient selection.

Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network.

The electrocardiogram (ECG) is a widely used medical test, consisting of voltage versus time traces collected from surface recordings over the heart1. Here we hypothesized that a deep neural network (DNN) can predict an important future clinical event, 1-year all-cause mortality, from ECG voltage-time traces. By using ECGs collected over a 34-year period in a large regional health system, we trained a DNN with 1,169,662 12-lead resting ECGs obtained from 253,397 patients, in which 99,371 events occurred. The model achieved an area under the curve (AUC) of 0.88 on a held-out test set of 168,914 patients, in which 14,207 events occurred. Even within the large subset of patients (n = 45,285) with ECGs interpreted as 'normal' by a physician, the performance of the model in predicting 1-year mortality remained high (AUC = 0.85). A blinded survey of cardiologists demonstrated that many of the discriminating features of these normal ECGs were not apparent to expert reviewers. Finally, a Cox proportional-hazard model revealed a hazard ratio of 9.5 (P < 0.005) for the two predicted groups (dead versus alive 1 year after ECG) over a 25-year follow-up period. These results show that deep learning can add substantial prognostic information to the interpretation of 12-lead resting ECGs, even in cases that are interpreted as normal by physicians.

Feasibility of blood testing combined with PET-CT to screen for cancer and guide intervention.

Cancer treatments are often more successful when the disease is detected early. We evaluated the feasibility and safety of multicancer blood testing coupled with positron emission tomography-computed tomography (PET-CT) imaging to detect cancer in a prospective, interventional study of 10,006 women not previously known to have cancer. Positive blood tests were independently confirmed by a diagnostic PET-CT, which also localized the cancer. Twenty-six cancers were detected by blood testing. Of these, 15 underwent PET-CT imaging and nine (60%) were surgically excised. Twenty-four additional cancers were detected by standard-of-care screening and 46 by neither approach. One percent of participants underwent PET-CT imaging based on false-positive blood tests, and 0.22% underwent a futile invasive diagnostic procedure. These data demonstrate that multicancer blood testing combined with PET-CT can be safely incorporated into routine clinical care, in some cases leading to surgery with intent to cure.

Using Adipose Measures from Health Care Provider-Based Imaging Data for Discovery.

The location and type of adipose tissue is an important factor in metabolic syndrome. A database of picture archiving and communication system (PACS) derived abdominal computerized tomography (CT) images from a large health care provider, Geisinger, was used for large-scale research of the relationship of volume of subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) with obesity-related diseases and clinical laboratory measures. Using a "greedy snake" algorithm and 2,545 CT images from the Geisinger PACS, we measured levels of VAT, SAT, total adipose tissue (TAT), and adipose ratio volumes. Sex-combined and sex-stratified association testing was done between adipose measures and 1,233 disease diagnoses and 37 clinical laboratory measures. A genome-wide association study (GWAS) for adipose measures was also performed. SAT was strongly associated with obesity and morbid obesity. VAT levels were strongly associated with type 2 diabetes-related diagnoses (p = 1.5 × 10-58), obstructive sleep apnea (p = 7.7 × 10-37), high-density lipoprotein (HDL) levels (p = 1.42 × 10-36), triglyceride levels (p = 1.44 × 10-43), and white blood cell (WBC) counts (p = 7.37 × 10-9). Sex-stratified tests revealed stronger associations among women, indicating the increased influence of VAT on obesity-related disease outcomes particularly among women. The GWAS identified some suggestive associations. This study supports the utility of pursuing future clinical and genetic discoveries with existing imaging data-derived adipose tissue measures deployed at a larger scale.

Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration.

Intracranial hemorrhage (ICH) requires prompt diagnosis to optimize patient outcomes. We hypothesized that machine learning algorithms could automatically analyze computed tomography (CT) of the head, prioritize radiology worklists and reduce time to diagnosis of ICH. 46,583 head CTs (~2 million images) acquired from 2007-2017 were collected from several facilities across Geisinger. A deep convolutional neural network was trained on 37,074 studies and subsequently evaluated on 9499 unseen studies. The predictive model was implemented prospectively for 3 months to re-prioritize "routine" head CT studies as "stat" on realtime radiology worklists if an ICH was detected. Time to diagnosis was compared between the re-prioritized "stat" and "routine" studies. A neuroradiologist blinded to the study reviewed false positive studies to determine whether the dictating radiologist overlooked ICH. The model achieved an area under the ROC curve of 0.846 (0.837-0.856). During implementation, 94 of 347 "routine" studies were re-prioritized to "stat", and 60/94 had ICH identified by the radiologist. Five new cases of ICH were identified, and median time to diagnosis was significantly reduced (p < 0.0001) from 512 to 19 min. In particular, one outpatient with vague symptoms on anti-coagulation was found to have an ICH which was treated promptly with reversal of anticoagulation, resulting in a good clinical outcome. Of the 34 false positives, the blinded over-reader identified four probable ICH cases overlooked in original interpretation. In conclusion, an artificial intelligence algorithm can prioritize radiology worklists to reduce time to diagnosis of new outpatient ICH by 96% and may also identify subtle ICH overlooked by radiologists. This demonstrates the positive impact of advanced machine learning in radiology workflow optimization.

Bariatric Left Gastric Artery Embolization for the Treatment of Obesity: A Review of Gut Hormone Involvement in Energy Homeostasis.

OBJECTIVE: The global population is becoming more overweight and obese, leading to increases in associated morbidity and mortality rates. Advances in catheter-directed embolotherapy offer the potential for the interventional radiologist to make a contribution to weight loss. Left gastric artery embolization reduces the supply of blood to the gastric fundus and decreases serum levels of ghrelin. Early evidence suggests that this alteration in gut hormone balance leads to changes in energy homeostasis and weight reduction. The pathophysiologic findings and current evidence associated with the use of left gastric artery embolization are reviewed. CONCLUSION: The prevalence of obesity continues to increase at an alarming rate, and, thus far, advances in medical management have been relatively ineffective in slowing this trend. Lifestyle modifications such as diet and exercise are effective initially, but most patients regain the weight in the long term. Bariatric surgery is the most effective strategy for achieving long-term weight loss; however, as with all surgical procedures, it has potential complications.

Contemporary comparison of surgical and interventional arteriography management of blunt renal injury.

PURPOSE: High-grade renal injuries have traditionally been treated operatively. Alternatively, embolotherapy is used to control hemorrhage, but there are few studies that validate this practice after renal injury. Embolotherapy may offer an effective and safe means to arrest hemorrhage after high-grade blunt renal injury. MATERIALS AND METHODS: Retrospective analysis was performed of high-grade renal injury (grade III or higher). Patients who were initially treated with arteriography were compared with those who underwent surgery. Statistical analysis was performed with Wilcoxon rank-sum and χ(2) tests. RESULTS: Sixty-nine patients were identified, 28 of whom had contrast agent extravasation on computed tomography (CT). Of these 69 patients, 17 underwent operation and 20 underwent arteriography. The surgical cohort had a higher injury severity score (39.6 vs 24.2; P < .01), but there was no difference in renal injury grade (P = .9). The arteriography cohort received significantly more contrast medium (P < .001). Contrast agent extravasation was confirmed angiographically in six of 12 patients who had this finding on CT, and embolotherapy controlled bleeding in all six. No significant difference was noted in transfusion need, recurrent hemorrhage, creatinine level at discharge, glomerular filtration rate, or length of stay (P > .4 for each endpoint). There was a trend toward a longer stay in the intensive care unit in the surgical cohort and a higher likelihood of discharge to home in the arteriography group (P = .08 for each endpoint). CONCLUSIONS: Embolotherapy offers a safe means to diagnose and arrest hemorrhage after renal injury. The additional contrast agent needed for imaging does not increase the incidence of nephropathy irrespective of renal injury grade.

Triple-lumen peripherally inserted central catheter in patients in the critical care unit: prospective evaluation.

PURPOSE: To prospectively evaluate outcomes associated with use of a triple-lumen (TL) peripherally inserted central catheter (PICC) in the intensive care unit (ICU) setting. MATERIALS AND METHODS: Patients were prospectively enrolled in this HIPAA-compliant, institutional review board-approved study. Informed consent was obtained. All patients were in one hospital's ICUs and needed intermediate-term central venous access requiring three lumina. A 6-F tapered TL PICC was placed by a bedside nursing-based team with backup from the Interventional Radiology department. Placement complications, as well as long-term complications, were recorded. At catheter removal, ultrasonography (US) of the veins containing the TL PICC was performed to detect occult venous thrombosis. Regardless of indication for removal, catheters were sent for culture to detect colonization. RESULTS: The study was stopped prematurely after 50 of a planned 167 patients were enrolled when a scheduled interim analysis detected a venous thrombosis rate that was considered unacceptably high by the study oversight committee (thrombosis was symptomatic in 20% of patients [10 of 50]). Venous thrombosis (symptomatic or asymptomatic) was detected in 26 of 45 patients (58%; 95% confidence interval [CI]: 43%, 72%) examined with US. Documented catheter-related bloodstream infection did not occur (0%; 95% CI: 0%, 7%); colonization was detected in three of 29 catheter tips sent for culture (10%; 95% CI: 2%, 27%). Catheter malfunction and dislodgment occurred in one patient each. CONCLUSION: The TL PICC design used in this study resulted in unacceptably high venous thrombosis rates. Even when used in a high-risk setting for infection (ie, the ICU), rates of clinically evident infection and colonization were absent and low, respectively.

Drainage of collections associated with hernia mesh: is it worthwhile?

PURPOSE: Synthetic mesh has revolutionized abdominal wall hernia repair. However, mesh infections present a clinical problem because the standard practice of surgical excision is fraught with increased morbidity. Here, single-institutional outcomes in managing mesh-related collections via percutaneous drainage are retrospectively reviewed to assess its effectiveness. MATERIALS AND METHODS: A total of 21 patients underwent drainage of perimesh collections. Three types of mesh were employed: polytetrafluoroethylene (PTFE; n = 5), polypropylene (n = 14), and porcine dermal collagen (n = 3). One patient received both polypropylene and PTFE. Drainage was performed with ultrasound guidance (n = 19) or surgical drain exchange (n = 2). Mesh type, culture results, fluid collection size, and location were analyzed with respect to need for mesh excision. RESULTS: Sixteen of 21 patients (76%) were successfully treated with drainage. One required additional surgical capsulectomy; the mesh was salvaged. Four required mesh excision because of recurrent infection (n = 2) or lack of improvement of clinical course (n = 2). Recurrent infection occurred in six patients, with mesh salvage via conservative management or new drainage in four. Fluid cultures were positive in 68% of patients (n = 13), with Staphylococcus aureus the most common organism. Cultures did not predict mesh excision (P = .26). The PTFE excision rate trended higher compared with polypropylene (40% vs 14%; P = .27). No porcine dermal collagen mesh was excised. Neither fluid collection size nor location predicted mesh excision. Mean follow-up was 319 days (range, 6-1,406 d). CONCLUSIONS: Percutaneous drainage of suspected mesh-related abscess is effective. The use of PTFE mesh trended toward a higher excision rate.

Short-term infection in cuffed versus noncuffed small bore central catheters: a randomized trial.

PURPOSE: To determine if a polyester cuff offered benefit in jugular small-bore central catheters (SBCCs). MATERIALS AND METHODS: Eighty-four patients were randomly assigned to receive a 5-F single- or 6-F dual-lumen SBCC with (n = 42) or without (n = 42) a polyester cuff. Follow-up was performed at 2 weeks, 1 month, and 3 months or at catheter removal, whichever came first. At scheduled follow-up, catheter function, patient satisfaction, and infection were determined. At catheter removal, tip culture was performed to determine colonization and jugular vein patency was determined with ultrasonography (US). RESULTS: The overall infection rate was 0.4 per 1,000 catheter days. There was one clinical infection (noncuffed catheter). Colonization occurred in two noncuffed catheters and one cuffed catheter. There was one catheter dislodgment in the noncuffed group and none in the cuffed group. Cuffed catheters were no more difficult to insert but took slightly longer to remove (6 minutes +/- 4.7 vs 5 minutes +/- 3, P = .39) and often required local anesthesia for removal, whereas noncuffed catheters did not (41% vs 0%, P = .001). Partial (two cuffed, 0 noncuffed) or complete (two cuffed, one noncuffed) jugular thrombosis was seen on five of 58 completion US studies (8.6%). CONCLUSIONS: A polyester cuff on a SBCC confers no significant benefit in short-term colonization rates. Infection in SBCCs is uncommon. Despite their small diameters, SBCCs can result in jugular thrombosis, an important consideration in any patient requiring long-term venous access.

The case for primary placement of tunneled hemodialysis catheters in acute kidney injury.

PURPOSE: Nontunneled hemodialysis catheters (NTDCs) are widely used for initial hemodialysis access in new-onset renal failure. The National Kidney Foundation recommends NTDC use for hemodialysis duration of less than 1 week in acute kidney injury because of the increased infection risk compared with tunneled hemodialysis catheters (TDCs) with longer use. The present study was performed to determine whether primary placement of TDCs in this setting is more appropriate, and whether there are predictors of recovery of renal function in less than 1 week. MATERIALS AND METHODS: In the authors' practice, patients referred to the interventional radiology unit in whom no contraindications exist receive a TDC; 76 patients who received a primary TDC for acute kidney injury and who eventually recovered renal function were retrospectively reviewed herein. Causes of renal failure, various renal function parameters, and demographics were collected, as were TDC dwell times, in an effort to determine predictors of recovery and/or extended duration of use. RESULTS: Mean TDC dwell time in patients who eventually recovered from acute kidney injury was 34 days; only 15 of 76 (20%) recovered within 1 week. At TDC placement, there were no significant differences between patients who recovered in less than (vs greater than) 1 week. CONCLUSIONS: The present results support primary placement of TDCs in patients with acute kidney injury who require hemodialysis and in whom no contraindications exist, as no predictors of recovery of renal function in less than 1 week were identified.

MRI of TIPS with covered stent-grafts: In vitro analysis using a flow phantom and initial clinical experience.

OBJECTIVE: The use of the Viatorr stent-graft for transjugular intrahepatic portosystemic shunt (TIPS) creation is thought to improve patency rates. This study was designed to show the feasibility and pitfalls of imaging Viatorr TIPS using MRI. CONCLUSION: MRI of the Viatorr stent-graft is feasible for the initial assessment of the TIPS. Familiarity with the imaging patterns will allow proper interpretation of the MRI findings. MRI also allows the depiction of detailed anatomic information concerning stent positioning.