Validation of Acute Pancreatitis Among Adults in an Integrated Healthcare System.

BACKGROUND: Acute pancreatitis is a serious gastrointestinal disease that is an important target for drug safety surveillance. Little is known about the accuracy of ICD-10 codes for acute pancreatitis in the United States, or their performance in specific clinical settings. We conducted a validation study to assess the accuracy of acute pancreatitis ICD-10 diagnosis codes in inpatient, emergency department (ED), and outpatient settings. METHODS: We reviewed electronic medical records for encounters with acute pancreatitis diagnosis codes in an integrated healthcare system from October 2015 to December 2019. Trained abstractors and physician adjudicators determined whether events met criteria for acute pancreatitis. RESULTS: Out of 1,844 eligible events, we randomly sampled 300 for review. Across all clinical settings, 182 events met validation criteria for an overall positive predictive value (PPV) of 61% (95% confidence intervals [CI] = 55, 66). The PPV was 87% (95% CI = 79, 92%) for inpatient codes, but only 45% for ED (95% CI = 35, 54%) and outpatient (95% CI = 34, 55%) codes. ED and outpatient encounters accounted for 43% of validated events. Acute pancreatitis codes from any encounter type with lipase >3 times the upper limit of normal had a PPV of 92% (95% CI = 86, 95%) and identified 85% of validated events (95% CI = 79, 89%), while codes with lipase <3 times the upper limit of normal had a PPV of only 22% (95% CI = 16, 30%). CONCLUSIONS: These results suggest that ICD-10 codes accurately identified acute pancreatitis in the inpatient setting, but not in the ED and outpatient settings. Laboratory data substantially improved algorithm performance.

The association between heart failure hospitalization and self-reported domains of health.

BACKGROUND: We compared self-reported domains of health between patients who with vs. without a recent heart failure (HF) hospitalization. METHODS: We fielded a 59-item questionnaire that included the 12-item Kansas City Cardiomyopathy Questionnaire (KCCQ-12) to age/sex-matched groups of 2000 HF patients who had and had not had a recent HF hospitalization. We entered questionnaire responses and electronic medical record data into multivariable logistic regression models to identify independent associations with a HF hospitalization. RESULTS: After two mailings, we received 468 completed questionnaires for response rate of 23.4%. Patients with a recent HF hospitalization had significantly lower scores on the KCCQ-12 Quality of Life (52.6 vs. 59.6, p = 0.016) and Social Limitations (48.4 vs. 55.5, p = 0.009) scales as well as the Clinical Summary Scale (50.8 vs. 55.3, p = 0.048) and Total KCCQ-12 score (49.6 vs. 56.8, p = 0.003). In sequential logistic regression models designed to achieve parsimony, Total KCCQ was a strong predictor of being in the recent hospitalization group. When using the KCCQ-12 sub-scales, the Social Limitations scale was a strong predictor of being in the recent hospitalization group. CONCLUSIONS: After accounting for comorbidities and other risk factors, a HF hospitalization appears to profoundly limit social activities which can increase the risk of poor outcomes.

Development of an algorithm to identify inpatient opioid-related overdoses and oversedation using electronic data.

PURPOSE: To facilitate surveillance and evaluate interventions addressing opioid-related overdoses, algorithms are needed for use in large health care databases to identify and differentiate community-occurring opioid-related overdoses from inpatient-occurring opioid-related overdose/oversedation. METHODS: Data were from Kaiser Permanente Northwest (KPNW), a large integrated health plan. We iteratively developed and evaluated an algorithm for electronically identifying inpatient overdose/oversedation in KPNW hospitals from 1 January 2008 to 31 December 2014. Chart audits assessed accuracy; data sources included administrative and clinical records. RESULTS: The best-performing algorithm used these rules: (1) Include events with opioids administered in an inpatient setting (including emergency department/urgent care) followed by naloxone administration within 275 hours of continuous inpatient stay; (2) exclude events with electroconvulsive therapy procedure codes; and (3) exclude events in which an opioid was administered prior to hospital discharge and followed by readmission with subsequent naloxone administration. Using this algorithm, we identified 870 suspect inpatient overdose/oversedation events and chart audited a random sample of 235. Of the random sample, 185 (78.7%) were deemed overdoses/oversedation, 37 (15.5%) were not, and 13 (5.5%) were possible cases. The number of hours between time of opioid and naloxone administration did not affect algorithm accuracy. When "possible" overdoses/oversedations were included with confirmed events, overall positive predictive value (PPV) was very good (PPV = 84.0%). Additionally, PPV was reasonable when evaluated specifically for hospital stays with emergency/urgent care admissions (PPV = 77.0%) and excellent for elective surgery admissions (PPV = 97.0%). CONCLUSIONS: Algorithm performance was reasonable for identifying inpatient overdose/oversedation with best performance among elective surgery patients.

Effect of Combination Cholesterol-Lowering Therapy and Triglyceride-Lowering Therapy on Medical Costs in Patients With Type 2 Diabetes Mellitus.

High triglyceride (TG) levels among patients with type 2 diabetes mellitus (DM) are associated with higher medical costs. We analyzed the economic impact of TG-lowering therapies and whether the association between medical costs and therapy differed according to TG reduction. We conducted an observational cohort study of 184,932 patients with diabetes mellitus who had a TG measurement between January 2012 and June 2013 and a second TG measurement 3 to 15 months later. We identified 4 therapy groups (statin monotherapy, TG-specific monotherapy, statin/TG-specific combination therapy, or no therapy) and stratified those groups by percent change in TG (increased ≥5%, change of ≤4.9%, decreased 5% to 29%, decreased ≥30%). We compared change in medical costs between the year before and after therapy, adjusted for demographic and clinical characteristics. Of the 184,932 total patients, 143,549 (77.6%) received statin monotherapy, 900 (0.5%) received TG-specific monotherapy, 1,956 (1.1%) received statin and TG-specific combination therapy, and 38,527 (20.8%) received no prescription lipid agents. After covariate adjustment, statin/TG-specific agent recipients had a mean 1-year total cost reduction of $1,110. The greatest cost reduction was seen among statin/TG-specific combination therapy patients who reduced TG levels by ≥30% (-$2,859). Statin monotherapy patients who reduced TG by ≥30% also had a large reduction in adjusted costs (-$1,079). In conclusion, we found a substantial economic benefit to treating diabetic patients with statin/TG-specific combination lipid therapy compared with monotherapy of either type or no lipid pharmacotherapy. A TG reduction of ≥30% produced a particularly large reduction in 1-year medical costs.