Development of a risk-adjusted in-hospital mortality prediction model for community-acquired pneumonia: a retrospective analysis using a Japanese administrative database

BMC Pulmonary Medicine

Yuichi Imanaka¹  Hiroshi Ikai¹  Noriko Sasaki¹  Susumu Kunisawa²  Hironori Uematsu¹ 

[1] Department of Healthcare Economics and Quality Management, Graduate School of Medicine, Kyoto University, Yoshida Konoe-cho, Sakyo-ku, Kyoto City, Kyoto 606-8501, Japan;Department of Biomedical Sciences, Ritsumeikan University, Norohigashi, Kusatsu City, Shiga 525-0058, Japan

关键词: Administrative database;    Scoring system;    Severity index;    Prognosis prediction model;    Risk-adjusted mortality;    Community-acquired pneumonia;   

Others  :  1090745
DOI  :  10.1186/1471-2466-14-203

 received in 2014-08-21, accepted in 2014-12-01,  发布年份 2014

【 摘 要 】

Background

Community-acquired pneumonia (CAP) is a common cause of patient hospitalization and death, and its burden on the healthcare system is increasing in aging societies. Here, we develop and internally validate risk-adjustment models and scoring systems for predicting mortality in CAP patients to enable more precise measurements of hospital performance.

Methods

Using a multicenter administrative claims database, we analyzed 35,297 patients hospitalized for CAP who had been discharged between April 1, 2012 and September 30, 2013 from 303 acute care hospitals in Japan. We developed hierarchical logistic regression models to analyze predictors of in-hospital mortality, and validated the models using the bootstrap method. Discrimination of the models was assessed using c-statistics. Additionally, we developed scoring systems based on predictors identified in the regression models.

Results

The 30-day in-hospital mortality rate was 5.8%. Predictors of in-hospital mortality included advanced age, high blood urea nitrogen level or dehydration, orientation disturbance, respiratory failure, low blood pressure, high C-reactive protein levels or high degree of pneumonic infiltration, cancer, and use of mechanical ventilation or vasopressors. Our models showed high levels of discrimination for mortality prediction, with a c-statistic of 0.89 (95% confidence interval: 0.89-0.90) in the bootstrap-corrected model. The scoring system based on 8 selected variables also showed good discrimination, with a c-statistic of 0.87 (95% confidence interval: 0.86-0.88).

Conclusions

Our mortality prediction models using administrative data showed good discriminatory power in CAP patients. These risk-adjustment models may support improvements in quality of care through accurate hospital evaluations and inter-hospital comparisons.

【 授权许可】

   
2014 Uematsu et al.; licensee BioMed Central.

【 图 表 】

Figure 1.

【 参考文献 】

• [1]Marrie TJ: Community-acquired pneumonia in the elderly. Clin Infect Dis 2000, 31(4):1066-1078.
• [2]McCabe C, Kirchner C, Zhang H, Daley J, Fisman DN: Guideline-concordant therapy and reduced mortality and length of stay in adults with community-acquired pneumonia: playing by the rules. Arch Intern Med 2009, 169(16):1525-1531.
• [3]Berwick DM, James B, Coye MJ: Connections between quality measurement and improvement. Med Care 2003, 41(1 Suppl):I30-I38.
• [4]Wu AW: The measure and mismeasure of hospital quality: appropriate risk-adjustment methods in comparing hospitals. Ann Intern Med 1995, 122(2):149-150.
• [5]Lim WS, van der Eerden MM, Laing R, Boersma WG, Karalus N, Town GI, Lewis SA, Macfarlane JT: Defining community acquired pneumonia severity on presentation to hospital: an international derivation and validation study. Thorax 2003, 58(5):377-382.
• [6]Sligl WI, Marrie TJ: Severe community-acquired pneumonia. Crit Care Clin 2013, 29(3):563-601.
• [7]Jones B, Gundlapalli AV, Jones JP, Brown SM, Dean NC: Admission Decisions and Outcomes of Community-Acquired Pneumonia in the Homeless Population: A Review of 172 Patients in an Urban Setting. Am J Public Health 2013, 103(S2):S289-S293.
• [8]Chalmers JD, Singanayagam A, Akram AR, Mandal P, Short PM, Choudhury G, Wood V, Hill AT: Severity assessment tools for predicting mortality in hospitalised patients with community-acquired pneumonia. Systematic review and meta-analysis. Thorax 2010, 65(10):878-883.
• [9]Shindo Y, Sato S, Maruyama E, Ohashi T, Ogawa M, Imaizumi K, Hasegawa Y: Comparison of severity scoring systems A-DROP and CURB-65 for community-acquired pneumonia. Respirology 2008, 13(5):731-735.
• [10]Rothberg MB, Pekow PS, Priya A, Zilberberg MD, Belforti R, Skiest D, Lagu T, Higgins TL, Lindenauer PK: Using highly detailed administrative data to predict pneumonia mortality. PLoS One 2014, 9(1):e87382.
• [11]Hamada H, Sekimoto M, Imanaka Y: Effects of the per diem prospective payment system with DRG-like grouping system (DPC/PDPS) on resource usage and healthcare quality in Japan. Health Policy 2012, 107(2):194-201.
• [12]Otake H, Yasunaga H, Horiguchi H, Matsutani N, Matsuda S, Ohe K: Impact of hospital volume on chest tube duration, length of stay, and mortality after lobectomy. Ann Thorac Surg 2011, 92(3):1069-1074.
• [13]Ohta T, Waga S, Handa W, Saito I, Takeuchi K: New grading of level of disordered consiousness (author's transl). No Shinkei Geka 1974, 2(9):623-627.
• [14]Sundararajan V, Henderson T, Perry C, Muggivan A, Quan H, Ghali WA: New ICD-10 version of the Charlson comorbidity index predicted in-hospital mortality. J Clin Epidemiol 2004, 57(12):1288-1294.
• [15]Singanayagam A, Singanayagam A, Chalmers JD: Obesity is associated with improved survival in community-acquired pneumonia. Eur Respir J 2013, 42(1):180-187.
• [16]Kohno S, Seki M, Watanabe A, CAP Study Group: Evaluation of an assessment system for the JRS 2005: A-DROP for the management of CAP in adults. Intern Med 2011, 50(11):1183-1191.
• [17]Harrell F, Lee KL, Mark DB: Tutorial in biostatistics multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 1996, 15:361-387.
• [18]Chassin MR, Hannan EL, DeBuono BA: Benefits and hazards of reporting medical outcomes publicly. N Engl J Med 1996, 334(6):394-398.
• [19]Lim WS, Baudouin SV, George RC, Hill AT, Jamieson C, Le Jeune I, Macfarlane JT, Read RC, Roberts HJ, Levy ML, Wani M, Woodhead MA: Pneumonia Guidelines Committee of the BTS Standards of Care Committee: BTS guidelines for the management of community acquired pneumonia in adults: update 2009. Thorax 2009, 64(Suppl 3):iii1-iii55.
• [20]Kwok CS, Loke YK, Woo K, Myint PK: Risk prediction models for mortality in community-acquired pneumonia: a systematic review. Biomed Res Int 2013, 2013:504136.
• [21]King P, Mortensen EM, Bollinger M, Restrepo MI, Copeland LA, Pugh MJ, Nakashima B, Anzueto A, Hitchcock Noel P: Impact of obesity on outcomes for patients hospitalised with pneumonia. Eur Respir J 2013, 41(4):929-934.
• [22]Ewig S, de Roux A, Bauer T, Garcia E, Mensa J, Niederman M, Torres A: Validation of predictive rules and indices of severity for community acquired pneumonia. Thorax 2004, 59(5):421-427.
• [23]Chalmers JD, Singanayagam A, Hill AT: C-reactive protein is an independent predictor of severity in community-acquired pneumonia. Am J Med 2008, 121(3):219-225.
• [24]Hasley PB, Albaum MN, Li Y, Fuhrman CR, Britton CA, Marrie TJ, Singer DE, Coley CM, Kapoor WN, Fine MJ: Do pulmonary radiographic findings at presentation predict mortality in patients with community-acquired pneumonia? Arch Intern Med 1996, 156(19):2206-2212.
• [25]Fine MJ, Auble TE, Yealy DM, Hanusa BH, Weissfeld LA, Singer DE, Coley CM, Marrie TJ, Kapoor WN: A prediction rule to identify low-risk patients with community-acquired pneumonia. N Engl J Med 1997, 336(4):243-250.
• [26]Rothberg MB, Pekow PS, Priya A, Lindenauer PK: Variation in Diagnostic Coding of Patients With Pneumonia and Its Association With Hospital Risk-Standardized Mortality Rates: A Cross-sectional Analysis. Ann Intern Med 2014, 160(6):380-388. doi:10.7326/M13-1419
• [27]Uchiyama N, Suda R, Yamao S, Tomishima Y, Jinta T, Nishimura N, Chohnabayashi N: A new severity score for community-acquired pneumonia: PARB score. Crit Care 2010, 14:1.