Variation in Diagnostic Coding of Patients With Pneumonia and Its Association With Hospital Risk-Standardized Mortality Rates

Rothberg, Michael B.; Pekow, Penelope S.; Priya, Aruna; Lindenauer, Peter K.

doi:10.7326/m13-1419

Cited by 52 publications

(63 citation statements)

References 14 publications

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“…Our findings support the recommendation that sepsis is an additional condition that warrants attention at the national level in the United States (34), based on the high frequency with which readmissions occur after sepsis and the associated morbidity and mortality. This consideration would be timely, given temporal trends in diagnostic coding (i.e., increased documentation of sepsis as the principal diagnosis for a hospitalization paired with decreased documentation of pneumonia) (35), due in part to incentives related to public reporting of 30-day risk-standardized mortality for pneumonia (36).…”

Section: Discussionmentioning

confidence: 99%

Post–Acute Care Use and Hospital Readmission after Sepsis

et al. 2015

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Rationale: The epidemiology of post-acute care use and hospital readmission after sepsis remains largely unknown.Objectives: To examine the rate of post-acute care use and hospital readmission after sepsis and to examine risk factors and outcomes for hospital readmissions after sepsis. Methods:In an observational cohort study conducted in an academic health care system (2010-2012), we compared post-acute care use at discharge and hospital readmission after 3,620 sepsis hospitalizations with 108,958 nonsepsis hospitalizations. We used three validated, claims-based approaches to identify sepsis and severe sepsis.Measurements and Main Results: Post-acute care use at discharge was more likely after sepsis, driven by skilled care facility placement (35.4% after sepsis vs. 15.8%; P , 0.001), with the highest rate observed after severe sepsis. Readmission rates at 7, 30, and 90 days were higher postsepsis (P , 0.001). Compared with nonsepsis hospitalizations (15.6% readmitted within 30 d), the increased readmission risk was present regardless of sepsis severity (27.3% after sepsis and 26.0-26.2% after severe sepsis). After controlling for presepsis characteristics, the readmission risk was found to be 1.51 times greater (95% CI, 1.38-1.66) than nonsepsis hospitalizations. Readmissions after sepsis were more likely to result in death or transition to hospice care (6.1% vs. 13.3% after sepsis; P , 0.001). Independent risk factors associated with 30-day readmissions after sepsis hospitalizations included age, malignancy diagnosis, hospitalizations in the year prior to the index hospitalization, nonelective index admission type, one or more procedures during the index hospitalization, and low hemoglobin and high red cell distribution width at discharge.Conclusions: Post-acute care use and hospital readmissions were common after sepsis. The increased readmission risk after sepsis was observed regardless of sepsis severity and was associated with adverse readmission outcomes.

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Section: Discussionmentioning

confidence: 99%

Post–Acute Care Use and Hospital Readmission after Sepsis

et al. 2015

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“…Independent variables for motherinfant dyads were entered as fixedeffect components, and the hospital identifier was entered as a random intercept component. 18 A full model was fit with all independent variables described above. Model selection for the fixed effects were performed by using backward elimination with preference given to models with lower Akaike information criterion.…”

Section: Discussionmentioning

confidence: 99%

“…Hospital-specific predicted and expected formula supplementation percentages were determined from the final model for each perinatal care level. 18 The risk-adjusted formula supplementation percentage for each hospital was calculated by multiplying the hospital-specific ratio for formula supplementation (predicted percentage divided by expected percentage) by the observed formula supplementation percentage for the corresponding perinatal care level. 18 We compared the betweenhospital variances for models of supplemental feeding before and after risk adjustment and calculated the IntraClass coefficient and the proportional change in variances.…”

Section: Discussionmentioning

confidence: 99%

“…18 The risk-adjusted formula supplementation percentage for each hospital was calculated by multiplying the hospital-specific ratio for formula supplementation (predicted percentage divided by expected percentage) by the observed formula supplementation percentage for the corresponding perinatal care level. 18 We compared the betweenhospital variances for models of supplemental feeding before and after risk adjustment and calculated the IntraClass coefficient and the proportional change in variances. 20,21 The predictive accuracy of the models was assessed by the area under receiver operating characteristic curve.…”

Section: Discussionmentioning

confidence: 99%

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Variation in Formula Supplementation of Breastfed Newborn Infants in New York Hospitals

et al. 2017

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OBJECTIVES: We examined the variation between 126 New York hospitals in formula supplementation among breastfed infants after adjusting for socioeconomic, maternal, and infant factors and stratifying by level of perinatal care. METHODS:We used 2014 birth certificate data for 160 911 breastfed infants to calculate hospital-specific formula supplementation percentages by using multivariable hierarchical logistic regression models. RESULTS:Formula supplementation percentages varied widely among hospitals, from 2.3% to 98.3%, and was lower among level 1 hospitals (18.2%) than higher-level hospitals (50.6%-57.0%). Significant disparities in supplementation were noted for race and ethnicity (adjusted odds ratios [aORs] were 1.54-2.05 for African Americans, 1.85-2.74 for Asian Americans, and 1.25-2.16 for Hispanics, compared with whites), maternal education (aORs were 2.01-2.95 for ≤12th grade, 1.74-1.85 for high school or general education development, and 1.18-1.28 for some college or a college degree, compared with a Master's degree), and insurance coverage (aOR was 1.27-1.60 for Medicaid insurance versus other). Formula supplementation was higher among mothers who smoked, had a cesarean delivery, or diabetes. At all 4 levels of perinatal care, there were exemplar hospitals that met the HealthyPeople 2020 supplementation goal of ≤14.2%. After adjusting for individual risk factors, the hospital-specific, risk-adjusted supplemental formula percentages still revealed a wide variation. CONCLUSIONS:A better understanding of the exemplar hospitals could inform future efforts to improve maternity care practices and breastfeeding support to reduce unnecessary formula supplementation, reduce disparities, increase exclusive breastfeeding and breastfeeding duration, and improve maternal and child health outcomes.

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“…[13] In addition, there is wide variation in hospitals' use of principal diagnoses of sepsis or respiratory failure in patients with pneumonia. [14] Between 2003 to 2009, one study discovered hospitalization and inpatient mortality rates for patients with a principal diagnosis of pneumonia decreased substantially. However, when the principal diagnoses of sepsis or respiratory failure with a secondary diagnosis of pneumonia were included, the declines in the hospitalization rate and inpatient mortality were attenuated; [15] this suggests an association of these results with temporal trends in diagnostic coding.…”

Section: Introductionmentioning

confidence: 99%

Missing the Mark: Inaccuracy of administrative data in identification of hospitalized patients with pneumonia and results of a systematic clinical reclassification process on readmission rates

Ruopp

Boggan

Holland

et al. 2017

JHA

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Objective: Pneumonia readmissions carry financial ramifications under the Hospital Readmissions Reduction Program (HRRP). As readmission determination utilizes administrative data, healthcare systems should evaluate accuracy of pneumonia diagnoses. We sought to develop a systemic process for pneumonia classification review and determine potential effects on pneumonia readmissions in a tertiary academic medical center in the United States. Methods: We performed independent reviews of all pneumonia discharges within 48 hours of discharge over a one-year period. We reclassified all pneumonia discharges into four categories based on the Centers for Disease Control and Prevention reference standard. Secondary review of discordant classifications was performed by discharging providers to determine final diagnosis. The primary outcome was readmission rate within 30 days by pneumonia clinical classification category. Results: Two hundred seventy-eight discharges were reviewed, with overall readmission rate of 18.0%. Independent review confirmed 191 cases (68.7%) as definite or probable pneumonia, while 87 cases (31.3%) were classified as either probably not or not pneumonia. Readmission rates differed significantly between cases reviewed as pneumonia vs. those reviewed as unlikely to be pneumonia (14.1% vs. 26.4%, p < .02). Discharging attending physicians agreed with independent reviewers in 58/87 cases (66.6%), attenuating readmission differences (rate 16.8% for those finalized as pneumonia vs. 22.4% for another diagnosis, p = .32). Pneumonia readmissions were reduced by 1.2% using the classification standard. Conclusions: Complex conditions such as pneumonia may be inaccurately diagnosed in many patients, potentially affecting penalties associated with readmission rates. Therefore, it is imperative that healthcare systems adopt systematic review processes to standardize diagnoses and improve comparative administrative data.

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Variation in Diagnostic Coding of Patients With Pneumonia and Its Association With Hospital Risk-Standardized Mortality Rates

Abstract: Agency for Healthcare Research and Quality.

Cited by 52 publications

References 14 publications

Post–Acute Care Use and Hospital Readmission after Sepsis

Post–Acute Care Use and Hospital Readmission after Sepsis

Variation in Formula Supplementation of Breastfed Newborn Infants in New York Hospitals

Missing the Mark: Inaccuracy of administrative data in identification of hospitalized patients with pneumonia and results of a systematic clinical reclassification process on readmission rates

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