Literature DB >> 18328309

Increased hospital mortality in patients with bedside hippus.

Joshua C Denny1, Frederick V Arndt, William D Dupont, Eric G Neilson.   

Abstract

BACKGROUND: Hippus is a prominent, repetitive oscillation of the pupils. Although regarded by some as a normal variant of pupillary unrest, the clinical importance of hippus has not been investigated systematically in hospitalized patients.
METHODS: We conducted a retrospective cohort study of 117 hospitalized patients demonstrating hippus. To mitigate observer bias, 486 control patients were selected using 2 adjacent admissions by the same attending physician before and after each index case. The primary outcomes were mortality during the admission and within 30 days of discharge.
RESULTS: Patients with bedside hippus were more likely to die within 30 days of observation (P <.00005). Independent risk factors for death by 30 days were altered mental status (odds ratio [OR] 4.11; 95% confidence interval [CI], 2.05-8.25, P <.001), hippus (OR 2.99; 95% CI, 1.46-6.11, P = .003), cirrhosis (P = .029), and renal disease (P = .054); angiotensin-system inhibitors were protective (P = .012). Patients with hippus were more likely to have altered mental status (OR 11.23; 95% CI, 6.27-20.09, P <.001), a history of trauma (OR 3.76; 95% CI, 1.65-8.59, P = .002), cirrhosis (P = .038), renal disease (P = .051), and a history of using iron supplements (P = .016).
CONCLUSION: The recognition of hippus in hospitalized patients is a clinically important predictor of early mortality.

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Year:  2008        PMID: 18328309     DOI: 10.1016/j.amjmed.2007.09.014

Source DB:  PubMed          Journal:  Am J Med        ISSN: 0002-9343            Impact factor:   4.965


  7 in total

1.  An analytical approach to characterize morbidity profile dissimilarity between distinct cohorts using electronic medical records.

Authors:  Jonathan S Schildcrout; Melissa A Basford; Jill M Pulley; Daniel R Masys; Dan M Roden; Deede Wang; Christopher G Chute; Iftikhar J Kullo; David Carrell; Peggy Peissig; Abel Kho; Joshua C Denny
Journal:  J Biomed Inform       Date:  2010-08-03       Impact factor: 6.317

2.  Data from clinical notes: a perspective on the tension between structure and flexible documentation.

Authors:  S Trent Rosenbloom; Joshua C Denny; Hua Xu; Nancy Lorenzi; William W Stead; Kevin B Johnson
Journal:  J Am Med Inform Assoc       Date:  2011-01-12       Impact factor: 4.497

Review 3.  Phenome-Wide Association Studies as a Tool to Advance Precision Medicine.

Authors:  Joshua C Denny; Lisa Bastarache; Dan M Roden
Journal:  Annu Rev Genomics Hum Genet       Date:  2016-05-04       Impact factor: 8.929

4.  PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations.

Authors:  Joshua C Denny; Marylyn D Ritchie; Melissa A Basford; Jill M Pulley; Lisa Bastarache; Kristin Brown-Gentry; Deede Wang; Dan R Masys; Dan M Roden; Dana C Crawford
Journal:  Bioinformatics       Date:  2010-03-24       Impact factor: 6.937

5.  Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data.

Authors:  Joshua C Denny; Lisa Bastarache; Marylyn D Ritchie; Robert J Carroll; Raquel Zink; Jonathan D Mosley; Julie R Field; Jill M Pulley; Andrea H Ramirez; Erica Bowton; Melissa A Basford; David S Carrell; Peggy L Peissig; Abel N Kho; Jennifer A Pacheco; Luke V Rasmussen; David R Crosslin; Paul K Crane; Jyotishman Pathak; Suzette J Bielinski; Sarah A Pendergrass; Hua Xu; Lucia A Hindorff; Rongling Li; Teri A Manolio; Christopher G Chute; Rex L Chisholm; Eric B Larson; Gail P Jarvik; Murray H Brilliant; Catherine A McCarty; Iftikhar J Kullo; Jonathan L Haines; Dana C Crawford; Daniel R Masys; Dan M Roden
Journal:  Nat Biotechnol       Date:  2013-12       Impact factor: 54.908

6.  Chapter 13: Mining electronic health records in the genomics era.

Authors:  Joshua C Denny
Journal:  PLoS Comput Biol       Date:  2012-12-27       Impact factor: 4.475

7.  Desiderata for computable representations of electronic health records-driven phenotype algorithms.

Authors:  Huan Mo; William K Thompson; Luke V Rasmussen; Jennifer A Pacheco; Guoqian Jiang; Richard Kiefer; Qian Zhu; Jie Xu; Enid Montague; David S Carrell; Todd Lingren; Frank D Mentch; Yizhao Ni; Firas H Wehbe; Peggy L Peissig; Gerard Tromp; Eric B Larson; Christopher G Chute; Jyotishman Pathak; Joshua C Denny; Peter Speltz; Abel N Kho; Gail P Jarvik; Cosmin A Bejan; Marc S Williams; Kenneth Borthwick; Terrie E Kitchner; Dan M Roden; Paul A Harris
Journal:  J Am Med Inform Assoc       Date:  2015-09-05       Impact factor: 4.497

  7 in total

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