RNA Editing Genes Associated with Extreme Old Age in Humans and with Lifespan in C. elegans

The strong familiality of living to extreme ages suggests that human longevity is genetically regulated. The majority of genes found thus far to be associated with longevity primarily function in lipoprotein metabolism and insulin/IGF-1 signaling. There are likely many more genetic modifiers of human longevity that remain to be discovered.Here, we first show that 18 single nucleotide polymorphisms (SNPs) in the RNA editing genes ADARB1 and ADARB2 are associated with extreme old age in a U.S. based study of centenarians, the New England Centenarian Study. We describe replications of these findings in three independently conducted centenarian studies with different genetic backgrounds (Italian, Ashkenazi Jewish and Japanese) that collectively support an association of ADARB1 and ADARB2 with longevity. Some SNPs in ADARB2 replicate consistently in the four populations and suggest a strong effect that is independent of the different genetic backgrounds and environments. To evaluate the functional association of these genes with lifespan, we demonstrate that inactivation of their orthologues adr-1 and adr-2 in C. elegans reduces median survival by 50%. We further demonstrate that inactivation of the argonaute gene, rde-1, a critical regulator of RNA interference, completely restores lifespan to normal levels in the context of adr-1 and adr-2 loss of function.Our results suggest that RNA editors may be an important regulator of aging in humans and that, when evaluated in C. elegans, this pathway may interact with the RNA interference machinery to regulate lifespan

Changes in Patient Perceptions of the Provider Most Involved in Care During COVID-19 and Corresponding Effects on Patient Trust

During COVID-19 routine clinical operations were disrupted, including limits on the types of providers allowed to perform in-person care and frequency of times they could enter a patient's room. Whether these changes affected patients’ trust in the care they received during hospitalization is unknown. Hospitalized patients on the general medicine service were called after discharge and asked to identify who (attending, resident, etc.) was most involved in their inpatient care, and how much trust they had in the physician caring for them. During the pandemic patients were more likely to report attending physicians (29% to 34%) and nurses (30% to 35%), and less likely to report residents/interns (8.1% to 6.5%) or medical students (1.7% to 1.4%) as most involved in their care (chi-squared test, p = 0.04). Patients reporting their attending physician as most involved in their care were more likely to report trusting their doctor (chi-squared test, p < 0.01). As such, trends in medical education that limit trainees’ time in direct patient care may affect the development of clinical and interpersonal skills necessary to establish patient trust

Applying Classification Trees to Hospital Administrative Data to Identify Patients with Lower Gastrointestinal Bleeding

Background: Lower gastrointestinal bleeding (LGIB) is a common cause of acute hospitalization. Currently, there is no accepted standard for identifying patients with LGIB in hospital administrative data. The objective of this study was to develop and validate a set of classification algorithms that use hospital administrative data to identify LGIB. Methods: Our sample consists of patients admitted between July 1, 2001 and June 30, 2003 (derivation cohort) and July 1, 2003 and June 30, 2005 (validation cohort) to the general medicine inpatient service of the University of Chicago Hospital, a large urban academic medical center. Confirmed cases of LGIB in both cohorts were determined by reviewing the charts of those patients who had at least 1 of 36 principal or secondary International Classification of Diseases, Ninth revision, Clinical Modification (ICD-9-CM) diagnosis codes associated with LGIB. Classification trees were used on the data of the derivation cohort to develop a set of decision rules for identifying patients with LGIB. These rules were then applied to the validation cohort to assess their performance. Results: Three classification algorithms were identified and validated: a high specificity rule with 80.1% sensitivity and 95.8% specificity, a rule that balances sensitivity and specificity (87.8% sensitivity, 90.9% specificity), and a high sensitivity rule with 100% sensitivity and 91.0% specificity. Conclusion: These classification algorithms can be used in future studies to evaluate resource utilization and assess outcomes associated with LGIB without the use of chart review.</p

Changes in Patient Perceptions of the Provider Most Involved in Care During COVID-19 and Corresponding Effects on Patient Trust

During COVID-19 routine clinical operations were disrupted, including limits on the types of providers allowed to perform in-person care and frequency of times they could enter a patient's room. Whether these changes affected patients’ trust in the care they received during hospitalization is unknown. Hospitalized patients on the general medicine service were called after discharge and asked to identify who (attending, resident, etc.) was most involved in their inpatient care, and how much trust they had in the physician caring for them. During the pandemic patients were more likely to report attending physicians (29% to 34%) and nurses (30% to 35%), and less likely to report residents/interns (8.1% to 6.5%) or medical students (1.7% to 1.4%) as most involved in their care (chi-squared test, p  = 0.04). Patients reporting their attending physician as most involved in their care were more likely to report trusting their doctor (chi-squared test, p  < 0.01). As such, trends in medical education that limit trainees’ time in direct patient care may affect the development of clinical and interpersonal skills necessary to establish patient trust

Applying Classification Trees to Hospital Administrative Data to Identify Patients with Lower Gastrointestinal Bleeding

<div><p>Background</p><p>Lower gastrointestinal bleeding (LGIB) is a common cause of acute hospitalization. Currently, there is no accepted standard for identifying patients with LGIB in hospital administrative data. The objective of this study was to develop and validate a set of classification algorithms that use hospital administrative data to identify LGIB.</p><p>Methods</p><p>Our sample consists of patients admitted between July 1, 2001 and June 30, 2003 (derivation cohort) and July 1, 2003 and June 30, 2005 (validation cohort) to the general medicine inpatient service of the University of Chicago Hospital, a large urban academic medical center. Confirmed cases of LGIB in both cohorts were determined by reviewing the charts of those patients who had at least 1 of 36 principal or secondary International Classification of Diseases, Ninth revision, Clinical Modification (ICD-9-CM) diagnosis codes associated with LGIB. Classification trees were used on the data of the derivation cohort to develop a set of decision rules for identifying patients with LGIB. These rules were then applied to the validation cohort to assess their performance.</p><p>Results</p><p>Three classification algorithms were identified and validated: a high specificity rule with 80.1% sensitivity and 95.8% specificity, a rule that balances sensitivity and specificity (87.8% sensitivity, 90.9% specificity), and a high sensitivity rule with 100% sensitivity and 91.0% specificity.</p><p>Conclusion</p><p>These classification algorithms can be used in future studies to evaluate resource utilization and assess outcomes associated with LGIB without the use of chart review.</p></div

Results from a classification tree specifying a prior lower gastrointestinal bleeding probability of 20%.

<p>Dx = diagnosis; LGIB = lower gastrointestinal bleeding</p><p>This tree provides 86.2% sensitivity and 96.7% specificity.</p

Receiver Operating Characteristic Curve.

<p>Each point along the curve represents a different classification tree generated by varying the prior probability of lower gastrointestinal bleeding (LGIB). The two solid points on the graph represent the trees that were selected to either maximize specificity (sensitivity = 86.2%, specificity = 96.7%) or to balance sensitivity and specificity (sensitivity = 92.3%, specificity = 92.3%).</p