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Estimation of the Excess mortality of nosocomial infections and iatrogenic events P otential P itfalls

Jean-François TIMSIT MD PhD Medical ICU Outcome of cancers and critical illnesses University Joseph Fourrier INSERM U 823 Grenoble FRANCE Medical ICU, hospital A Michallon. Estimation of the Excess mortality of nosocomial infections and iatrogenic events P otential P itfalls.

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Estimation of the Excess mortality of nosocomial infections and iatrogenic events P otential P itfalls

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  1. Jean-François TIMSIT MD PhD Medical ICU Outcome of cancers and critical illnesses University Joseph Fourrier INSERM U 823 Grenoble FRANCE Medical ICU, hospital A Michallon Estimation of the Excess mortality of nosocomial infections and iatrogenic events Potential Pitfalls Freiburg; March 9th 2009

  2. Excess risk of death due to infectious diseases • Necrotizing pneumonia due to CA-MRSA PVL + in a 18-year-old man with no underlying illness • MRSA VAP occurring after 20 days of mechanical ventilation in a 70 yo men with multiple organ failure after a CABG

  3. Iatrogenic events as a quality indicator? • Frequent event • Ease and reproducibility of the diagnosis • Surveillance easy • Avoidable • High morbidity and mortality Quality indicator study group; ICHE 1995;16:308

  4. Potential pitfalls • Inappropriate diagnosis of exposure • Inappropriate selection of unexposed population • Inappropriate way of taking into account confounding factors • Inappropriate way of taking into account the time of the exposure and time of occurrence of the event and other competing events

  5. Assumptions a. Exposed and non-exposed groups are representative samples of a well-defined population at risk b. Absence of "exposure" also well defined and assumed to be maintained in the non-exposed group during the course of the study c. Information is collected from exposed and unexposed patients in the same way

  6. % Death

  7. Excess risk of death of ventilator-associated pneumonia Exposed-unexposed studies † † † † (*) Matched according to severity on admission (†) Matched according to severity and duration of exposure to the risk

  8. Excess risk of death of ventilator-associated pneumonia Exposed-unexposed studies † † † † (*) Matched according to severity on admission (†) Matched according to severity and duration of exposure to the risk

  9. Diagnosis of exposure

  10. The true rate of nosocomial pneumonia is 20% in our ICUWe decide to modify our diagnostic procedure switching from a qualitative tracheal aspirate (High Sensitivity: 90%, and a low specificity 70%) to a less sensitive (70%) and more specific (90%) testWhat will be the consequences on the estimated rate of VAP in my ICU?

  11. = 0.9 X 80 = 0.9 X 20 True rate vs estimated rate of an event 72 18 Se=p[T+]/[D+]= 90% Sp=p[T-]/[D-]= 90% Rate of VAP: 26% Se=p[T+]/[D+]= 1 Sp=p[T-]/[D-]= 1 Rate of VAP: 20%

  12. Estimated Rate= 32% 32 Estimated rate according to Sensitivity and specificity of the test True rate of VAP= 20% Se=80% Sp=80% Se of the test Specificity 100% Specificity 90% Specificity 60% Specificity 80% Specificity 70% Specificity 50% Estimated rate (%)

  13. Estimated rate according to Sensitivity and specificity of the test True rate of VAP= 20% Se=70% Sp=90% Estimated Rate= 21% Se of the test Specificity 100% Specificity 90% Specificity 60% Specificity 80% Specificity 70% Specificity 50% Estimated rate (%) Se=90% Sp=70% Estimated Rate= 43.5%

  14. The « true » associated mortality of VAP is 20% (No VAP death rate:20%), you want to estimate the attributable mortality of VAP in your hospital. You will use your new diagnostic test. You want to plan your case-cohort study…What will be your hypothesis about the estimated attributable mortality in your hospital? • 20% • The attributable mortality will slightly rise because the rate of VAP will increase • The attributable mortality will slightly decrease because the rate of VAP will decrease • The attributable mortality will be approximately two-fold lower

  15. = 0.9 X 80 = 0.7X 20 True rate vs estimated rate of an event Sp=p[T+]/[D+]= 90% Se=p[T-]/[D-]= 70% Estimated Rate of VAP: 22% Sp=p[T+]/[D+]= 1 Se=p[T-]/[D-]= 1 Rate of VAP: 20%

  16. =72 X 0.2 + 6X0.4death rate 21% =8 X 0.2 + 14X0.4death rate 33% Death rate 20% Death rate 40% Associated mortality Sp=p[T+]/[D+]= 90% Se=p[T-]/[D-]= 70% True rate of VAP: 20% Estimated rate:22%

  17. Associated mortality according to the accuracy of the test Hypothesis Rate of VAP 20% Sensitivty 60% 70% 80% 90% 100% Sp 90% Se 70% Specificity Specificity Specificity Attributable Death Death if true VAP: 40% Death if « true » non VAP: 20% True associated mortality= 20%

  18. Very very good test Attrib death = 22 %!!! Se=Sp=95% The error is much more pronounced if the event is rare… Death with candidemia  80% Death without = 20% True rate of candidemia 3% Sensitivty Specificity 90% Specificity 95% Specificity 100% Attributable Death In these cases very good specificity  1 is needed +++

  19. Or very frequent… Death  80% Death without = 20% True rate 80% Sensitivity Specificity 100% Attributable Death In these cases very good sensitivity  1 is needed +++

  20. How to reduce measurement-of-exposure errors in longitudinal cohort studies? • Selection of a study group(s) that could lead to reduced measurement error • Use of repeated measurements to reduce measurement error (uncorrelated error) • Quality control • Detailed study manual • Standardized training of monitoring of data collectors • Problem with decision to limit the treatment (no diagnostic effort : • 12.6% of the Outcomerea databases patients • 50% of the decedents…

  21. How to deal with confounders? Variable independently associated with: • The exposure of interest • The outcome Distort results of the analysis

  22. Control of confounders In the study design • Restriction • Matching In the analysis • Stratification • Multivariate analysis

  23. The aim of the matching or adjustment processes..Is to obtain exposed and unexposed patients similar in everything except in the nosocomial infection…. Not me! I’ve got NI

  24. Disadvantages of matching • Cannot examine risks associated with matching variable • No controls identified, loose case data and vice versa • Could lead to bias • if excluded pop. differed from included pop. • And if excess-risk of death is different between strata.

  25. Matching or adjusting? • We can adjust on many confounders: • - log-linearity of the continuous variables • The risk of NI increase similarly between 20 and 40 year-old than between 50 and 70…. • Interaction between independent variables • But • Matching can control for confounding factors that are difficult to measure

  26. The homogeneity of the effect between strata is a key point. Overall: 40 + 1 + 1 + 1 4  10% 40% 1% 1% 1% Overall: 1 + 1 + 1 3 = 1% 1% 1% 1%

  27. Methods • Prospective cohort- Outcomerea group- 4 ICUs • Population: MV >= 5d • Severity of illnes recorded every days • Cox with VAP as a time dpdte variable. Stratifying the analysis by center • Interaction between center effect and HR estimation: Gail & Simon test

  28. 754 Pts >5 days of MV– 89 late onset VAP

  29. First modelb Second modelc pvalue HR (95% CI) pvalue HR (95% CI) MacCabe score >1 <10-4 2.43 (1.72-3.44) <10-4 2.12 (1.51-2.99) SAPS II score <10-4 1.03 (1.02-1.04) <10-4 1.03 (1.02-1.04) Increase in SAPS between D1 and D2d 0.004 1.56 (1.16-2.11) <10-4 2.01 (1.48-2.74) Increase in SAPS between D2 and D3d <10-4 1.84 (1.40-2.50) <10-4 1.97 (1.44-2.69) Increase in SAPS between D3 and D4d <10-4 1.80 (1.33-2.44) 0.004 1.56 (1.16-2.11) Late-onset pneumonia occurrenceb 0.04 1.54 (1.10-2.30) - - Late-onset pneumonia appropriately treatedc - - 0.27 1.44 (0.75-2.76) Late-onset pneumonia inappropriately treatedc - - 0.022 1.69 (1.08-2.65) Over-risk of death associated with VAP: Adequacy of initial antibiotic treatement Moine et al – ICM 2002; 28:154

  30. Late-onset pneumonia in four different ICUs * P aeruginosa or Acinetobacter species

  31. CONCLUSIONS • Late-onset VAP are associated with an increased in the risk of death (HR=1.54) • The risk estimate vary according to centers, even after carefull adjustment on other potential confounders (severity of illness and evolution during the first 4 days, micro-organisms, imediacy of an adequate treatment). • This result might partly explained discrepancies between articles in the literature

  32. Severity of the acute illness is a confounder Severity Death AEs

  33. Severity of the illness as a risk factor of nosocomial infection • Underlying illness • Charlson index, • Mc Cabe, ASA, Knaus, cancer... • Cause of admission • Severity • SAPSII, APACHE, MPM... • SOFA, LOD, MODS.. • Use of invasive procedures  Death

  34. AM J RESPIR CRIT CARE MED 1999;159:1249–1256. « A control had to match a patient in the following criteria in order to be considered a possible match: medical/surgical status, time in ICU prior to the development of VAP, duration of mechanical ventilation prior to VAP, Day 1 APACHE II score ( +-4 points), and MOD score on the day prior to development of VAP (+-3 points). » What do you think about matching on APACHE II score at Day 1 (ICU admission)? What do you think about the adjustment on severity just one day before VAP?

  35. Severity at admission??…The usual way is to adjust or match on severity scores at admission • However, it is probably not useful because • Severity scores have not been built for populations exposed to nosocomial infections • Because, independently of severity on admission, evolution of severity before the nosocomial infection is also a confounder

  36. Discrimination of a severity score (SAPSII) according to the previous duration of stay Suistomaa M et al - Intensive Care Med 2002; 28:479-85.

  37. P aeruginosaNosocomial pneumoniaRello CID 1996 • APACHE II is not able to discriminate survivors or decedents after P. aeruginosa NP… * *

  38. Choice of good adjustment covariates (matching criterias) • If the covariate is associated with exposure (NI) and event (death)  improvement in the validity (effect of the confounding factor is taken into account) • If not related to the exposure (NI) but with death no improvement in validity nor efficiency • If covariates is associated only with NI and not death  no improvement in validity but a decrease in efficiency (power)

  39. AM J RESPIR CRIT CARE MED 1999;159:1249–1256. « A control had to match a patient in the following criteria in order to be considered a possible match: medical/surgical status, time in ICU prior to the development of VAP, duration of mechanical ventilation prior to VAP, Day 1 APACHE II score ( +-4 points), and MOD score on the day prior to development of VAP (+-3 points). » What do you think about matching on an organ dysfonction score on the day prior to VAP?

  40. PRO!!!! • Evolution of severity is a risk factor of death and is a risk factor of nosocomial infection

  41. Evolution of severity after admission and NI risk • 41 patients: • First infection: • 26 UTI • 8 NP • 4 bacteremias • 3 CRI • Matched (1:1) on APACHE II, Age, Duration of exposure to the risk Girou et al AJRCCM 1998; 157:1151

  42. Very quickly after admission, before NI severity is different Death:44% Death: 14.6% Girou et al AJRCCM 1998; 157:1151

  43. Evolution of severity and risk of death Change in organ failure scores is independently associated with the risk of death Ferreira et al – JAMA 2001; 1754 The most accurate prediction is the one which is performed the latest Rué et al – Crit Care Med 2001; 45

  44. CON!!! • Matching (or adjusting) the day before the event is possibly an over-matching(over-adjustment) • The severity scores are partly related to NI not yet diagnosed…

  45. What is the good cut-point between appropriate matching and over-matching ? Estimations: Not ajusted: HR 2.06 (1.16-3.68) Adjusted on severity factors on admission: HR=2.01 (1.08-3.73) Adjusted on severity 7 days before NI HR=1.41 (0.76-2.61) Adjusted on severity 7 and 3 days before NI HR= 1.3 (0.69-2.46) SAPS II * * • Exposed • Unexposed Patients who will acquire NI are more severe than the ones who do not, even one week before the event … Soufir et al ICHE, 1999;20:396

  46. Severity as a confounding factor: possible solutions? • Use severity scores of the population at risk… • Population which stay more than 72 hours… • Measured at the 72th hour!!! • TRIO score (Timsit et al - Intens care Med 2001) • Use of severity scores measured during ICU stay before the occurrence of the event of interest…(3, 5, 7 days before?????) • Daily calibration? (Timsit et al – Crit Care Med 2002, Rué et al – Crit Care Med 2002)

  47. Potential pitfalls • Inappropriate diagnosis of exposure • Inappropriate selection of unexposed population • Inappropriate way of taking into account confounding factors • Inappropriate way of taking into account the time of the exposure and time of occurrence of the event and other competing events

  48. Time pitfalls • Time to measurement of exposure • Time fixed vs time-dependent covariates • Time-to-exposure: in exposed-unexposed study, an exposed patient is considered exposed even before the exposure! • Competing events

  49. Estimation de survie Kaplan Meier • être encore en vie après un instant t, c’est être en vie juste avant cet instant t et ne pas mourir à cet instant. • P(VV à t) /VV juste avant t niest le nombre de sujets à risque à l’instant ti et di est le nombre de décès au temps ti.

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