Improving Prediction of Survival for Extremely Premature Infants Born at 23 to 29 Weeks Gestational Age in the Neonatal Intensive Care Unit: Development and Evaluation of Machine Learning Models

BACKGROUND

Infants born at extremely preterm gestational ages are typically admitted to the neonatal intensive care unit (NICU) after initial resuscitation. The subsequent hospital course can be highly variable, and despite counseling aided by available risk calculators, there are significant challenges with shared decision-making regarding life support and transition to end-of-life care. Improving predictive models can help providers and families navigate these unique challenges.

OBJECTIVE

Machine learning methods have previously demonstrated added predictive value for determining intensive care unit outcomes, and their use allows consideration of a greater number of factors that potentially influence newborn outcomes, such as maternal characteristics. Machine learning-based models were analyzed for their ability to predict the survival of extremely preterm neonates at initial admission.

METHODS

Maternal and newborn information was extracted from the health records of infants born between 23 and 29 weeks of gestation in the Medical Information Mart for Intensive Care III (MIMIC-III) critical care database. Applicable machine learning models predicting survival during the initial NICU admission were developed and compared. The same type of model was also examined using only features that would be available prepartum for the purpose of survival prediction prior to an anticipated preterm birth. Features most correlated with the predicted outcome were determined when possible for each model.

RESULTS

Of included patients, 37 of 459 (8.1%) expired. The resulting random forest model showed higher predictive performance than the frequently used Score for Neonatal Acute Physiology With Perinatal Extension II (SNAPPE-II) NICU model when considering extremely preterm infants of very low birth weight. Several other machine learning models were found to have good performance but did not show a statistically significant difference from previously available models in this study. Feature importance varied by model, and those of greater importance included gestational age; birth weight; initial oxygenation level; elements of the APGAR (appearance, pulse, grimace, activity, and respiration) score; and amount of blood pressure support. Important prepartum features also included maternal age, steroid administration, and the presence of pregnancy complications.

CONCLUSIONS

Machine learning methods have the potential to provide robust prediction of survival in the context of extremely preterm births and allow for consideration of additional factors such as maternal clinical and socioeconomic information. Evaluation of larger, more diverse data sets may provide additional clarity on comparative performance.

Chemical Entity Normalization for Successful Translational Development of Alzheimer's Disease and Dementia Therapeutics

Background

Identifying chemical mentions within the Alzheimer's and dementia literature can provide a powerful tool to further therapeutic research. Leveraging the Chemical Entities of Biological Interest (ChEBI) ontology, which is rich in hierarchical and other relationship types, for entity normalization can provide an advantage for future downstream applications. We provide a reproducible hybrid approach that combines an ontology-enhanced PubMedBERT model for disambiguation with a dictionary-based method for candidate selection.

Results

There were 56,553 chemical mentions in the titles of 44,812 unique PubMed article abstracts. Based on our gold standard, our method of disambiguation improved entity normalization by 25.3 percentage points compared to using only the dictionary-based approach with fuzzy-string matching for disambiguation. For our Alzheimer's and dementia cohort, we were able to add 47.1% more potential mappings between MeSH and ChEBI when compared to BioPortal.

Conclusion

Use of natural language models like PubMedBERT and resources such as ChEBI and PubChem provide a beneficial way to link entity mentions to ontology terms, while further supporting downstream tasks like filtering ChEBI mentions based on roles and assertions to find beneficial therapies for Alzheimer's and dementia.

Identification of patient characteristics associated with survival benefit from metformin treatment in patients with stage I non-small cell lung cancer

BACKGROUND

Non-small cell lung cancer (NSCLC) continues to be a major cause of cancer deaths. Previous investigation has suggested that metformin use can contribute to improved outcomes in NSCLC patients. However, this association is not uniform in all analyzed cohorts, implying that patient characteristics might lead to disparate results. Identification of patient characteristics that affect the association of metformin use with clinical benefit might clarify the drug's effect on lung cancer outcomes and lead to more rational design of clinical trials of metformin's utility as an intervention. In this study, we examined the association of metformin use with long-term mortality benefit in patients with NSCLC and the possible modulation of this benefit by body mass index (BMI) and smoking status, controlling for other clinical covariates.

METHODS

This was a retrospective cohort study in which we analyzed data from the Veterans Affairs (VA) Tumor Registry in the United States. Data from all patients with stage I NSCLC from 2000 to 2016 were extracted from a national database, the Corporate Data Warehouse that captures data from all patients, primarily male, who underwent treatment through the VA health system in the United States. Metformin use was measured according to metformin prescriptions dispensed to patients in the VA health system. The association of metformin use with overall survival (OS) after diagnosis of stage I NSCLC was examined. Patients were further stratified according to BMI and smoking status (previous vs current) to examine the association of metformin use with OS across these strata.

RESULTS

Metformin use was associated with improved survival in patients with stage I NSCLC (average hazard ratio, 0.82; P < .001). An interaction between the effect of metformin use and BMI on OS was observed (χ2 = 3268.42; P < .001) with a greater benefit of metformin use observed in patients as BMI increased. Similarly, an interaction between smoking status and metformin use on OS was also observed (χ2 = 2997.05; P < .001) with a greater benefit of metformin use observed in previous smokers compared with current smokers.

CONCLUSIONS

In this large retrospective study, we showed that a survival benefit is enjoyed by users of metformin in a robust stage I NSCLC patient population treated in the VA health system. Metformin use was associated with an 18% improved OS. This association was stronger in patients with a higher BMI and in previous smokers. These observations deserve further mechanistic study and can help rational design of clinical trials with metformin in patients with lung cancer.

Modeling pandemic to endemic patterns of SARS-CoV-2 transmission using parameters estimated from animal model data

The contours of endemic coronaviral disease in humans and other animals are shaped by the tendency of coronaviruses to generate new variants superimposed upon nonsterilizing immunity. Consequently, patterns of coronaviral reinfection in animals can inform the emerging endemic state of the SARS-CoV-2 pandemic. We generated controlled reinfection data after high and low risk natural exposure or heterologous vaccination to sialodacryoadenitis virus (SDAV) in rats. Using deterministic compartmental models, we utilized in vivo estimates from these experiments to model the combined effects of variable transmission rates, variable duration of immunity, successive waves of variants, and vaccination on patterns of viral transmission. Using rat experiment-derived estimates, an endemic state achieved by natural infection alone occurred after a median of 724 days with approximately 41.3% of the population susceptible to reinfection. After accounting for translationally altered parameters between rat-derived data and human SARS-CoV-2 transmission, and after introducing vaccination, we arrived at a median time to endemic stability of 1437 (IQR = 749.25) days with a median 15.4% of the population remaining susceptible. We extended the models to introduce successive variants with increasing transmissibility and included the effect of varying duration of immunity. As seen with endemic coronaviral infections in other animals, transmission states are altered by introduction of new variants, even with vaccination. However, vaccination combined with natural immunity maintains a lower prevalence of infection than natural infection alone and provides greater resilience against the effects of transmissible variants.

Modeling pandemic to endemic patterns of SARS-CoV-2 transmission using parameters estimated from animal model data

The contours of endemic coronaviral disease in humans and other animals are shaped by the tendency of coronaviruses to generate new variants superimposed upon nonsterilizing immunity. Consequently, patterns of coronaviral reinfection in animals can inform the emerging endemic state of the SARS-CoV-2 pandemic. We generated controlled reinfection data after high and low risk natural exposure or heterologous vaccination to sialodacryoadenitis virus (SDAV) in rats. Using deterministic compartmental models, we utilized in vivo estimates from these experiments to model the combined effects of variable transmission rates, variable duration of immunity, successive waves of variants, and vaccination on patterns of viral transmission. Using rat experiment-derived estimates, an endemic state achieved by natural infection alone occurred after a median of 724 days with approximately 41.3% of the population susceptible to reinfection. After accounting for translationally altered parameters between rat-derived data and human SARS-CoV-2 transmission, and after introducing vaccination, we arrived at a median time to endemic stability of 1437 (IQR = 749.25) days with a median 15.4% of the population remaining susceptible. We extended the models to introduce successive variants with increasing transmissibility and included the effect of varying duration of immunity. As seen with endemic coronaviral infections in other animals, transmission states are altered by introduction of new variants, even with vaccination. However, vaccination combined with natural immunity maintains a lower prevalence of infection than natural infection alone and provides greater resilience against the effects of transmissible variants.

Using Artificial Intelligence With Natural Language Processing to Combine Electronic Health Record's Structured and Free Text Data to Identify Nonvalvular Atrial Fibrillation to Decrease Strokes and Death: Evaluation and Case-Control Study

BACKGROUND

Nonvalvular atrial fibrillation (NVAF) affects almost 6 million Americans and is a major contributor to stroke but is significantly undiagnosed and undertreated despite explicit guidelines for oral anticoagulation.

OBJECTIVE

The aim of this study is to investigate whether the use of semisupervised natural language processing (NLP) of electronic health record's (EHR) free-text information combined with structured EHR data improves NVAF discovery and treatment and perhaps offers a method to prevent thousands of deaths and save billions of dollars.

METHODS

We abstracted 96,681 participants from the University of Buffalo faculty practice's EHR. NLP was used to index the notes and compare the ability to identify NVAF, congestive heart failure, hypertension, age ≥75 years, diabetes mellitus, stroke or transient ischemic attack, vascular disease, age 65 to 74 years, sex category (CHA₂DS₂-VASc), and Hypertension, Abnormal liver/renal function, Stroke history, Bleeding history or predisposition, Labile INR, Elderly, Drug/alcohol usage (HAS-BLED) scores using unstructured data (International Classification of Diseases codes) versus structured and unstructured data from clinical notes. In addition, we analyzed data from 63,296,120 participants in the Optum and Truven databases to determine the NVAF frequency, rates of CHA₂DS₂‑VASc ≥2, and no contraindications to oral anticoagulants, rates of stroke and death in the untreated population, and first year's costs after stroke.

RESULTS

The structured-plus-unstructured method would have identified 3,976,056 additional true NVAF cases (P<.001) and improved sensitivity for CHA₂DS₂-VASc and HAS-BLED scores compared with the structured data alone (P=.002 and P<.001, respectively), causing a 32.1% improvement. For the United States, this method would prevent an estimated 176,537 strokes, save 10,575 lives, and save >US $13.5 billion.

CONCLUSIONS

Artificial intelligence-informed bio-surveillance combining NLP of free-text information with structured EHR data improves data completeness, prevents thousands of strokes, and saves lives and funds. This method is applicable to many disorders with profound public health consequences.

Longitudinal K-means approaches to clustering and analyzing EHR opioid use trajectories for clinical subtypes

Identification of patient subtypes from retrospective Electronic Health Record (EHR) data is fraught with inherent modeling issues, such as missing data and variable length time intervals, and the results obtained are highly dependent on data pre-processing strategies. As we move towards personalized medicine, assessing accurate patient subtypes will be a key factor in creating patient specific treatment plans. Partitioning longitudinal trajectories from irregularly spaced and variable length time intervals is a well-established, but open problem. In this work, we present and compare k-means approaches for subtyping opioid use trajectories from EHR data. We then interpret the resulting subtypes using decision trees, examining how each subtype is influenced by opioid medication features and patient diagnoses, procedures, and demographics. Finally, we discuss how the subtypes can be incorporated in static machine learning models as features in predicting opioid overdose and adverse events. The proposed methods are general, and can be extended to other EHR prescription dosage trajectories.

Improvements in pain, medication use and quality of life in onabotulinumtoxinA-resistant chronic migraine patients following erenumab treatment - real world outcomes

Background

The CGRP antagonists offer a novel therapeutic approach in migraine. Their utility in patients with severe forms of chronic migraine is a subject of particular interest. We present outcomes of 9 months of erenumab treatment in a cohort of patients with difficult-to-control chronic migraine, all of whom had prior unsatisfactory response to onabotulinumtoxinA.

Methods

We offered erenumab to 98 patients with a prior unsatisfactory response to onabotulinumtoxinA. Eighty of 98 had trialled greater occipital nerve injections (82%), 32/98 peripheral neurostimulation (33%) and 18/98 intravenous dihydroergotamine (18%). Thirty eight of 98 (39%) met the definition of triptan overuse and 43/98 (44%) analgesic overuse. All patients met the EHF criteria for ‘resistant migraine’. Outcome measures (recorded monthly) included days with headache limiting activities of daily living (“red”), not limiting (“amber”), headache free (“green”), and requiring triptans or other analgesics. Quality of life scores - headache impact test 6 (HIT-6), patient health questionnaire 9 (PHQ-9) and pain disability index (PDI) - were also measured.

Results

Mean number of red days improved by − 6.4 days (SE 0.67, 95%CI − 7.7 to − 5.1, p=0.001) at 3 months; − 6.8 days (SE 0.96, 95%CI − 8.80 to − 4.9, p=0.001) at 6 months and − 6.5 days (SE 0.86, 95%CI − 8.3 to − 4.8, p=0.001) at 9 months. Repeated measures ANOVA confirmed improvements in the number of red (p=0.001), green (p=0.001), triptan (p=0.001) and painkiller days (p=0.001) as well as scores of the HIT-6 (p=0.001), PHQ-9 (p=0.001), and PDI (p=0.001) across the duration of study.

Conclusion

We observed improvements in pain, medication use and quality of life in onabotulinumtoxinA-resistant chronic migraine patients following erenumab treatment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s10194-020-01214-2.

Assessing Opioid Use Patient Representations and Subtypes

Precision medicine, diagnosis, treatment, and prevention that accounts for natural human variability, can be beneficial for complex populations. The opioid user population is heterogeneous, characterized by many disorders, medications, and procedures. Using Electronic Health Record data, we create a patient representation, finding similarities between structured data, and then cluster the patients into patient subtypes. These subtypes can then be used for subsequent analysis.

Rosacea Patients Are at Higher Risk for Obstructive Sleep Apnea: Automated Retrospective Research

Using big data science we employ NLP and a novel interface the BMI Investigator to answer clinically meaninful questions. The use case presented is the association between Rosacea and Obstructive Sleep Apnea.

Treatment of Ebola-related critical illness

PURPOSE

To explore contemporary clincial case management of patients with Ebola virus disease.

METHODS

A narrative review from a clinical perspective of clinical features, diagnostic tests, treatments and outcomes of patients with Ebola virus disease.

RESULTS

Substantial advances have been made in the care of patients with Ebola virus disease (EVD), precipitated by the unprecedented extent of the 2014-2016 outbreak. There has been improved point-of-care diagnostics, improved characterization of the clinical course of EVD, improved patient-optimized standards of care, evaluation of effective anti-Ebola therapies, administration of effective vaccines, and development of innovative Ebola treatment units. A better understanding of the Ebola virus disease clinical syndrome has led to the appreciation of a central role for critical care clinicians-over 50% of patients have life-threatening complications, including hypotension, severe electrolyte imbalance, acute kidney injury, metabolic acidosis and respiratory failure. Accordingly, patients often require critical care interventions such as monitoring of vital signs, intravenous fluid resuscitation, intravenous vasoactive medications, frequent diagnostic laboratory testing, renal replacement therapy, oxygen and occasionally mechanical ventilation.

CONCLUSION

With advanced training and adherence to infection prevention and control practices, clinical interventions, including critical care, are feasible and safe to perform in critically ill patients. With specific anti-Ebola medications, most patients can survive Ebola virus infection.

Principles and Practices of Establishing a Hospital-Based Ebola Treatment Unit

Outbreaks of Ebola virus disease and high-risk transmissible infections are increasing and pose threats to health care workers and global health systems. Previous outbreaks offer lessons for health system preparedness and response, including establishment of hospital-based high-risk pathogen treatment units. Their creation demands early preparation and interprofessional coordination; infection prevention and control; case management training; prepositioning of supplies; conversion of existing structures to treatment units; and strengthening communication and research platforms. Hospital-based Ebola and high-risk pathogen treatment units may improve case detection, interrupt transmission, and improve staff safety and patient care.

Expanding Evolutionary Terminology Auditing with Historic Formal and Linguistic Intensions: A Case Study in SNOMED CT

A method is described to use SNOMED CT's history mechanism as a means to compute how the formal and linguistic intensions of its concepts change over versions. As a result of this, it is demonstrated that the intended principle of concept permanence is not always adhered to. It is shown that the evolution of formal intensions can be monitored fully automatically and that the proposed procedure includes a method to suggest missing subsumers in a concept's transitive closure set by identifying mistakes that have been made in the past. Changes in linguistic intensions were found to be much more labor-intensive to identify. It is suggested that this could be improved if the history mechanism would come with more detailed motivations for change than the current and insufficiently used annotation to the effect that a fully specified name 'fails to comply with the current editorial guidance'.

Neurological effects of glucocerebrosidase gene mutations

The association between Gaucher disease (GD) and Parkinson disease (PD) has been described for almost two decades. In the biallelic state (homozygous or compound heterozygous) mutations in the glucocerebrosidase gene (GBA) may cause GD, in which glucosylceramide, the sphingolipid substrate of the glucocerebrosidase enzyme (GCase), accumulates in visceral organs leading to a number of clinical phenotypes. In the biallelic or heterozygous state, GBA mutations increase the risk for PD. Mutations of the GBA allele are the most significant genetic risk factor for idiopathic PD, found in 5%–20% of idiopathic PD cases depending on ethnicity. The neurological consequences of GBA mutations are reviewed and the proposition that GBA mutations result in a disparate but connected range of clinically and pathologically related neurological features is discussed. The literature relating to the clinical, biochemical and genetic basis of GBA PD, type 1 GD and neuronopathic GD is considered highlighting commonalities and distinctions between them. The evidence for a unifying disease mechanism is considered.

Changes in implicit alcohol attitudes across adolescence, and associations with emerging alcohol use: Testing the reciprocal determinism hypothesis

Implicit alcohol-related cognitions develop during adolescence and are thought to play an important role in the etiology of adolescent alcohol use. Rooted in reciprocal determinism, a developmental theory of alcohol-related cognitions, the current study sought to enhance our understanding of the development of automatic alcohol associations and their relationship with alcohol use. To provide a theoretically aligned test of reciprocal determinism, we used latent change score models to examine whether growth in automatic alcohol associations and alcohol use was related to each other (between-person effects) and whether each construct led to changes in the other over time (within-person effects). Adolescents (N = 378) completed 4 annual assessments, spanning early to middle adolescence. Automatic alcohol associations were assessed with a Single Category Implicit Association Test, and we used a quadruple processing tree model to extract a more "process pure" index of these associations. Alcohol use increased from early to middle adolescence, as negative automatic alcohol associations weakened over that same time period. Although there was no support for between-person associations, on the within-person level, weak negative automatic alcohol associations at Waves 2 and 3 were associated with increases in drinking at subsequent waves. Alcohol use did not significantly predict changes in automatic alcohol associations. Findings suggest the utility of distinguishing within- and between-person associations to understand the development of automatic alcohol associations and that automatic alcohol associations are prospectively associated with alcohol use and a potential target for intervention, one that becomes an increasingly salient influence on drinking as adolescence progresses. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Biomedical Informatics Investigator

The BMI Investigator is a computer human interface built in .Net which allows simultaneous query of structured data such as demographics, administrative codes, medications (coded in RxNorm), laboratory test results (coded in LOINC) and formerly unstructured data in clinical notes (coded in SNOMED CT). The ontology terms identified using SNOMED are all coded as either positive, negative or uncertain assertions. They are then where applicable built into compositional expressions and stored in both a graph database and a triple store. The SNOMED CT codes are stored in a NOSQL database, Berkley DB, and the structured data is stored in SQL using the OMOP/OHDSI format. The BMI investigator also lets you develop models for cohort selection (data driven recruitment to clinical trials) and automated retrospective research using genomic criteria and we are adding image feature data currently to the system. We performed a usability experiment and the users identified some usability flaws which were used to improve the software. Overall, the BMI Investigator was felt to be usable by subject matter experts. Next steps for the software are to integrate genomic criteria and image features into the query engine.

Safe Opioid Prescription: A SMART on FHIR Approach to Clinical Decision Support

Background Prescription opioid pain medication overuse, misuse and abuse have been a significant contributing factor in the opioid epidemic. The rising death rates from opioid overdose have caused healthcare practitioners and researchers to work on optimizing pain therapy and limiting the prescriptions for pain medications. The state of New York has implemented a prescription drug monitoring program(PDMP), amended public health law to limit the prescription of opioids for acute pain and utilized the resources of the state and county health departments to help in curbing this epidemic. The recent publication of guidelines for prescription opioids from CDC [1] and ASIPP (American Society of Interventional pain practitioners) have independently reviewed literature and found good evidence of limiting opioid prescription for acute and chronic non cancer pain. [2] Method Over the last decade, advanced technology has increased the complexity of electronic health records systems leading to the development of Clinical Decision Support Systems (CDSS) to aid the work flow of healthcare providers. There are several systematic reviews on the effectiveness and utility of CDSSs. A common consensus is that commercially and locally developed CDSS are effective in improving patient measures while actual workload improvement and efficient cost-cutting measure are not significantly improved by CDSS. Patient provider involvement in developing CDSS is a determinant of its success and utilization rates. [7] Therefore, a plug and play form of CDSS which can be implemented from an external platform through secure channels would be more effective. Design The Health Level Seven's (HL7) open licensed interoperability standard Fast Health Interoperability Resources (FHIR) has a platform, Substitutable Medical Applications and Reusable Technologies (SMART) for CDSS app development by a third party. [3] We adopted these open source standard to plan to develop an app for accessible and efficient implementation of the recently published guidelines for management of pain with prescription opioid medications.

AIM

Mechanism of action of and mechanism of reduced susceptibility to the novel anti-Clostridium difficile compound LFF571

LFF571 is a novel semisynthetic thiopeptide and potent inhibitor of Gram-positive bacteria. We report that the antibacterial activity of LFF571 against Clostridium difficile is due to inhibition of translation. Single-step mutants of C. difficile with reduced susceptibility to LFF571 were selected at frequencies of <4.5 × 10(-11) to 1.2 × 10(-9). Sequencing revealed a G260E substitution in the thiopeptide-binding pocket of elongation factor Tu. Importantly, this mutation did not confer cross-resistance to clinically used antimicrobials. These results support the development of LFF571 as a treatment for C. difficile infection.

High prevalence of distal sensory polyneuropathy in antiretroviral-treated and untreated people with HIV in Tanzania

OBJECTIVES

To describe the prevalence of distal sensory polyneuropathy (DSP), a complication of both advanced HIV disease and of antiretroviral therapy (ART), amongst Tanzanians with HIV, on and off ART (including stavudine) with CD4 counts above and below 200 cells/μl.

METHODS

We recruited participants attending ART clinic into four groups: >6 months ART exposure and (i) CD4 < 200 cells/μl or (ii) CD4 > 200 cells/μl (ART/CD4 < 200 and ART/CD4 > 200, respectively); ART-naïve and (iii) CD4 < 200 cells/μl or iv)CD4 > 200 cells/μl (noART/CD4 < 200 and noART/CD4 > 200, respectively). Primary outcome was DSP, as defined by presence of at least one symptom and one sign.

RESULTS

Of 326 evaluable participants, 81 (32 men, median age 38 years, median CD4 142 cells/μl) were enrolled in the ART/CD4 < 200 group, 78 (17 men, median age 37 years, median CD4 345 cells/μl) in ART/CD4 > 200, 81 (30 men, median age 37 years, median CD4 128 cells/μl) in noART/CD4 < 200 and 86 (22 men, median age 33 years, median CD4 446 cells/μl) in noART/CD4 > 200. Numbness was the most commonly reported symptom. DSP prevalence ranged from 43.2% in ART/CD4 < 200 to 20.9% in noART/CD4 > 200. DSP was more common among men (adjusted odds ratio [aOR] 1.9, 95% confidence interval [CI] 1.2-3.3) and older participants (aOR 2.7, 95% CI 1.1-6.2 for age 40 + vs. <30 years).

CONCLUSION

Distal sensory polyneuropathy is common amongst those attending this clinic, even those with no ART exposure and a CD4 count above 200 cells/μl. Stavudine and didanosine expose HIV-infected patients to an additional avoidable risk of DSP. Access to non-neurotoxic ART regimes as well as earlier HIV diagnosis and initiation of ART is needed.

Enhanced recovery of cryopreserved islets using SIS

Although cryopreservation of pancreatic islets would add flexibility to transplantation, the recoveries are only 60% to 90% and function is decreased. Islets are multicellular structures approximately 50 to 250 microm in diameter organized into a network of cells and vascular channels. Due to this complexity, islets are more susceptible to damage during cryopreservation than an individual cell. This study investigated porcine small intestinal submucosa (SIS) as a matrix to support islets recovery and function post-thaw. Groups of frozen/thawed human islets (150 IE/condition; n = 4 preparations) were cultured for 5 weeks in plates containing noncoated Biopore membrane inserts alone or inserts covered with SIS. Islets were placed directly on the insert post-thaw (SIS(1)), or cultured overnight in standard plates, washed, and then transferred to the SIS (SIS(2)). Function was assessed by determining glucose-stimulated release of insulin, which was measured by radioimmunoassay. Analysis of basal insulin secretion showed time and treatment to be significantly different (P =.0043 and P =.0123, respectively) but without an interaction (P >.05). The two SIS treatments were not significantly different (P >.05); however, both SIS(1) and SIS(2) were significantly different from controls (P =.0108 and P =.0420, respectively). Similar results were obtained for stimulation indices; time and treatment were significantly different (P =.0161 and P =.0264, respectively) but not an interaction (P >.05). The two SIS treatments were not significantly different (P =.05); however, both SIS(1) and SIS(2) differed from controls (P =.0248 and P =.0407, respectively). The results indicate that SIS enables frozen-thawed islets to exhibit superior post-thaw function compared with a non-SIS-supported condition.

Differences between American Indian and non-Indian children referred for psychological services

The physical and social characteristics of 60 American Indian children referred for psychological services were compared to those of 60 matched, non-Indian controls. Data were obtained from detailed records available in a multidisciplinary, medical school-related child study clinic. Indian children exhibited more health and social risk factors, but were superior to non-Indians on a variety of motor variables. Interpretations are offered concerning better psychological services for American Indian children based on better understanding of their possible exposure to physical health and social risks which may be related to psychological development.

Survival of medically treated patients in the coronary artery surgery study (CASS) registry

The objective of this study was to evaluate the impact on survival of the anatomic extent of obstructive coronary artery disease and of two measures of left ventricular (LV) performance. This study is based on 20,088 patients without previous coronary artery bypass graft surgery who were enrolled in the registry of the National Heart, Lung, and Blood Institute Coronary Artery Surgery Study from 1975 to 1979. The cumulative 4-year survival of medically managed patients was analyzed to determine the survival of specific subsets of patients with obstructive coronary disease. The vital status of 99.8% of the patients was known. The 4-year survival of medically treated patients with no significant obstructive disease was 97%, in contrast to 92%, 84% and 68% in patients with one-, two- and three-vessel disease, respectively. The presence of left main coronary artery disease decreased survival significantly. The 4-year survival decreased from 70% to 60% in patients with three-vessel disease when significant obstruction of the left main coronary artery was also present. Patients with significant coronary artery disease who had an ejection fraction of 50--100%, 35--49%, and 0--34% had a 4-year survival of 92%, 83% and 58%, respectively. The systolic contraction pattern was assessed in five selected segments and given a score of 1--6, with a score of 1 for normal function, increasing to 6 if an aneurysm was present. In a patient with normal LV contraction in all five segments of the LV ventricular angiogram, the LV score would equal 5. Patients with an LV score of 5--11, 12--16 and 17--30 had 4-year survivals of 90%, 71% and 53%, respectively. Patients with good LV function (a score of 5--11) had a 4-year survival of 94%, 91% and 79% for one-, two- and three-vessel disease, respectively. Patients with poor left ventricular function (score of 17--30) had a 4-year survival rate of 67%, 61% and 42% in one-, two- and three-vessel disease, respectively. Thus, LV function is a more important predictor of survival than the number of diseased vessels.