Using the Electronic Medical Record to Improve Evidence-based Medical Practice. P. Brian Smith Duke University Medical Center Durham, NC

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1 Using the Electronic Medical Record to Improve Evidence-based Medical Practice P. Brian Smith Duke University Medical Center Durham, NC

2 Disclosure I have no relevant financial relationships with the manufacturer of any commercial products or providers of commercial services discussed in this CME activity I will discuss unapproved or investigative use of commercial products or devices

3 Objectives To review potential advantages of using EMR data To describe limitations of using EMR data To describe example of how use of EMR data has impacted patient care

4 Roadblocks to Randomized Trials in Premature Infants Ethical, legal, social, psychological Timing of consent Contamination Long-term follow-up Expense Time ~ 7 years 2 years for design, funding, start-up 3 years for enrollment 2 years for follow-up

5 NICHD - Neonatal Research Network 18 center, NIH sponsored network Observational studies and randomized trials Primarily focused on premature infants

6 Neonatal Research Network Randomized Trials in Premature Infants 100% Infants with primary outcome 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% OR = 0.89 (0.89, 0.99)

7 Outcomes by Center, week infants, Neonatal Research Network, % Infants with primary outcome Low center High center Smith, Pediatrics, 2012.

8 Cohort Studies Advantages Inexpensive ~20% cost of RCT if prospective ~1% cost of RCT if retrospective Timelines Years if prospective < 6 months if retrospective Disadvantages Biased results Hard to study rare outcomes

9 Problems with Getting the Data from the Electronic Medical Record Text variables Combining data Definitions change over time Dates/times Accuracy Missingness

10 Text Variables ACADEMIC PERFORMANCE DENGUE FEVER DISLOCATED RIGHT KNEE DROVE INTO TREE WHILE PLAYING FOOTBALL, Dental Pain Secondary to Braces EXCISION OF INGROWN TOENAIL FEELING "TIRED" FEELING HOT FEVER 37.2? FINGER TRAUMA FOOD POISONING FOREIGN OBJECT IN LEFT THIGH (SPLINTER) GIDDINESS GYDDINESS

11 Combining Data What variable do you use for matching? Medical record number Date of birth Name How do you detect duplicates? What if data does not match? Subject in A and B Subject in A but not B Subject in B but no A What if one or both dataset have more than 1 observation per subject?

12 Definitions may change <1500 g, Physicians 2270 infant with BPD 834 (37%) did not have BPD based on current definitions (NICHD Neonatal Research Network) Using NIH definition infants with BPD 5032 (78%) did not have BPD based on physician diagnosis Pediatrix Medical Group

13 Dates/Times - Formatting Mar /1/2011 1/3/2011 3/1/11 3/1/11;12:54pm 3/1/11;1354 March 1 st, 2011

14 Accuracy Extreme values (outliers) ga Percentiles Smallest 1% % % Obs % Sum of Wgt % 37 Mean Largest Std. Dev % % Variance % Skewness % Kurtosis

15 Missingness sex Freq. Percent Cum. 2, Female 6, Male 7, Unknown female male Total 16,

16 Controlling for Bias in Cohort Studies Regression Propensity Scoring

17 Propensity Scoring Conditional probability of receiving an exposure given a set of measured confounders collapse variables into single probability Match on propensity score

18 Propensity Scoring Advantages Distribution of confounders often better than randomization Good strategy when outcome of interest is rare Disadvantages Does not correct for unmeasured confounders Lose subjects that are not matched (*usually extreme data points)

19 Pediatrix Medical Group Pediatrix Medical Group 278 NICUs 25% of infants admitted to NICUs in the US Electronic Medical Record (EMR) - facilitates research, education, and quality improvement initiatives. Data are collected on admission and daily until discharge. Contains detailed information >850,000 infants >16,000,000 patient days

20 Anaerobic therapy and Clinical Outcomes in Very Low Birth Weight Infants with Necrotizing Enterocolitis Randomized trial of 42 infants showed that clindamycin therapy associated with stricture formation VLBW infants with medical or surgical NEC from 322 NICUs Antibiotic exposure on the first day of NEC Primary outcome - mortality or intestinal stricture. Infants treated with anaerobic therapy matched (1:1) with infants without anaerobic therapy using propensity score: ventilator support, FiO2, inotropic support, NEC stage (medical or surgical), postnatal age, gestational age (GA), small for GA status, sex, race, 5 min APGAR, discharge year and center Faix, J Peds, 1988.

21

22 n (%) Anaerobic therapy No 1248 Yes 1248 Gestational age (weeks) < (15) 190 (15) (55) 691 (55) 0.90 > (30) 367 (29) Small for gestational age 255 (20) 252 (20) 0.88 Male 672 (54) 673 (54) 0.97 Day of life on day 1 of NEC (8) 768 (62) 378 (30) 85 (7) 765 (61) 398 (32) P 0.37 NEC stage Medical 919 (74) 922 (74) 0.89 Surgical 329 (26) 326 (26) Mechanical ventilation on day 1 of NEC 687 (55) 678 (54) 0.72 Inotropic support on day 1 of NEC 173 (14) 173 (14) >0.99 Highest FiO2 on day 1 of NEC mean (5 th,95 th percentile) 38 (21,100) 37 (21,100) 0.34

23 Trends in Caffeine Use and Association between Clinical Outcomes and Timing of Therapy in Very-Low- Birth-Weight Infants Compare infants receiving early caffeine therapy (<3 days of life) and late caffeine therapy ( 3 days of life) 62,056 VLBW infants who received caffeine Propensity score GA, BW, sex, race, small for GA, Apgar score, antenatal steroids, outborn, center, birth year, apnea on DOL 0 or 1, respiratory support on DOL 1, FiO 2 on DOL 1, HFOV on DOL 1.

24 Baseline Characteristics PS Matched Patients Early Caffeine n=14,535 Late Caffeine n=14,535 P Birthweight, mean g (5 th, 95 th %tile) 1055 (630, 1447) 1054 (590, 1460) 0.77 GA, mean wk (5 th, 95 th %tile) 28.1 (25.0, 31.0) 28.0 (24.0, 32.0) 0.70 Sex, male 7386 (50.8) 7395 (50.9) 0.92 Any antenatal steroids 11,427 (78.6) 11,475 (79.0) 0.49 Respiratory support on DOL1 Room air 1242 (8.5) 1255 (8.6) Hood oxygen 56 (0.4) 78 (0.5) Nasal cannula 482 (3.3) 440 (3.0) HFNC 544 (3.7) 572 (3.9) CPAP 3249 (22.4) 3145 (21.6) Vent 7230 (49.7) 7465 (51.4) HFOV 1732 (11.9) 1580 (10.9) Maximal FiO 2 on DOL1 mean (5 th, 95 th %tile) 0.27 (0.21, 0.50) 0.27 (0.21, 0.45) 0.74 Dobson, J Peds, 2014.

25 Effect on Bronchopulmonary dysplasia (BPD) or Death PS Matched Patients Primary Outcomes Early Caffeine Late Caffeine Odds Ratio P n=14,535 n=14,535 (99% CI) BPD or death 3681 (27.6) 4591 (34.0) 0.74 (0.69, 0.80) <0.001 BPD in survivors 3070 (23.1) 4154 (30.7) 0.68 (0.63, 0.73) <0.001 Death 659 (4.5) 542 (3.7) 1.23 (1.05, 1.43) <0.001

26 Meropenem Safety 200 infant, 20 site PK and safety trial (MPODS-PI Benjamin) Cohorts based on gestational age (GA) and postnatal age (PNA) Seizures were adverse event of special interest In discussion with the FDA used the Pediatrix EMR - to determine if the proportion of infants with seizure in the trial was in excess of what would be expected MPODS inclusion criteria to identify infants in the Pediatrix data <91 days of age intra-abdominal infection creatinine <1.8 mg/dl no history of seizures Meropenem vs. Imipenem, death or seizure OR*=0.77 (0.62, 0.95) *Adjusted for GA, year, inotropic support, FiO2, ventilator support, meningitis Cohen-Wolkowiez, CID, Hornik, PIDJ, 2013.

27 Broad Spectrum Antibiotics and Candidiasis 3702 ELBW infants 3 rd generation cephalosporin/carbapenem Increased candidiasis, OR = 2.2 Multivariable logistic regression 128,914 infants cefotaxime vs. gentamicin Increased mortality, OR = 1.5 Multivariable logistic regression Cotten, Pediatrics, Clark, Pediatrics, 2006.

28 NICU - Broad spectrum antibiotic use Aliaga, Pediatrics, 2012.

29 NICU - Incidence of invasive candidiasis Aliaga, SPR 2012.

30 Positive blood culture (/1000 patient days) Duke NICU - RAIN campaign

31 Practice Changes You May Wish to Make Take advantage of your EMR You spend enough time entering data into it, you might as well get something out of it. Be thoughtful with how to handle outliers and missing data Be careful with nonrandomized data But don t always ignore it

32 Acknowledgements Pediatrix Reese Clark Dan Ellsbury Robert Ursprung Alan Spitzer Duke Mike Cotten Danny Benjamin

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