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First published online on May 2, 2007
Chest, doi:10.1378/chest.06-2233
doi:10.1378/chest.06-2233
(Chest. 2007; 131:1735-1741)
© 2007 American College of Chest Physicians
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Platelet Count Decline*

An Early Prognostic Marker in Critically Ill Patients With Prolonged ICU Stays

Delphine Moreau, MD; Jean-François Timsit, MD, PhD; Aurélien Vesin, MS; Maité Garrouste-Orgeas, MD; Arnaud de Lassence, MD{dagger}; Jean-Ralph Zahar, MD; Christophe Adrie, MD, PhD; François Vincent, MD; Yves Cohen, MD, PhD; Benoît Schlemmer, MD and Elie Azoulay, MD, PhD{ddagger}

* From the Medical ICU (Drs. Moreau, Schlemmer, and Azoulay), Saint Louis Teaching Hospital, Paris, France; the Medical ICU (Drs. Timsit and Vesin), Michallon Teaching Hospital, Grenoble, France; the Medical-Surgical ICU (Dr. Garrouste-Orgeas), Saint Joseph Teaching Hospital, Paris, France; the Medical-Surgical ICU (Dr. de Lassence), Louis Mourier Teaching Hospital, Colombes, France; the Microbiology Unit (Dr. Zahar), Necker Hospital, Paris, France; the Medical ICU (Dr. Adrie), Hôpital de La Fontaine, Saint Denis, France; and the Medical-Surgical ICU (Dr. Vincent) and Service de Réanimation (Dr. Cohen), Avicenne Teaching Hospital, Bobigny, France. {dagger} Deceased. {ddagger} A complete list of members of the Outcomerea Study Group is located in the Appendix.

Correspondence to: Elie Azoulay, MD, PhD, Medical ICU, Saint Louis Teaching Hospital, 1 Ave Claude Vellefaux, 75010 Paris, France; e-mail: elie.azoulay{at}outcomerea.org

Abstract

Background: Thrombocytopenia is common in ICU patients. The objective of this study was to evaluate possible links between declining platelet counts early in the ICU stay and survival.

Methods: All patients who were admitted to the ICU for at least 5 days and had no thrombocytopenia at the time of admission were included in the study. A multivariable logistic regression model, with hospital mortality as the outcome variable, was built.

Results: We included 1,077 patients in the study. At ICU admission, the median platelet count was not significantly different in survivors (256 x 109 cells/L; interquartile range [IQR], 206 to 330 x 109 cells/L) and nonsurvivors (262 x 109 cells/L; 211 to 351 x 109 cells/L). Median simplified acute physiology scores II (SAPS II) at ICU admission was worse in nonsurvivors than in survivors (50 [IQR, 37 to 63] vs 37 [IQR, 27 to 48], respectively; p < 0.0001), as was the mean (± SD) sequential organ failure assessment (SOFA) score on day 3 (6.3 ± 3.24 vs 4 ± 2.8, respectively; p < 0.0001). Absolute platelet counts were lowest on day 4, but differed significantly between survivors and nonsurvivors only on day 7. Conversely, any percentage decline in platelet counts from 10 to 60% on day 4 was significantly associated with mortality. By multivariable analysis, a 30% decline in platelet count independently predicted death (odds ratio, 1.54; 95% confidence interval, 1.12 to 2.14; p = 0.008), in addition to increasing or stable SOFA scores from ICU admission to day 4, older age, male gender, ICU admission for coma, worse SAPS II score at ICU admission, transfer from another ward, and comorbidity.

Conclusion: In patients who spend > 5 days in the ICU and have normal platelet counts at ICU admission, a decline in platelet counts provides prognostic information. This parameter deserves to be included in new scoring systems.

Key Words: ICU • mortality • outcome • platelet count • severity score

Low platelet counts are common in critically ill patients, at ICU admission or during a stay in the ICU. The incidence of thrombocytopenia in ICU patients has ranged from 15 to 58%, depending on the type of population and the threshold used to define thrombocytopenia.1234 Thrombocytopenia was associated with decreased survival in several studies.1234 Most of the studies investigating the prognostic significance of thrombocytopenia focused on baseline platelet counts, although the development of thrombocytopenia in the ICU may hold greater prognostic significance.245

Many ICU patients do not have thrombocytopenia at ICU admission but experience decreases in platelet count in the ICU that often fall short of the criteria for thrombocytopenia. The pathophysiologic and prognostic significance of these declines in platelet count is unclear. After major surgical procedures, platelet counts decrease, then recover and overshoot the normal range within a few days.67 Little is known about platelet-count declines after ICU admission for other reasons. Potential associations between platelet-count declines and survival have not been assessed in a large cohort of criticallyill patients with normal platelet counts at ICU admission.

We hypothesized that the platelet-count decline may be more important than the absolute platelet count for predicting survival in critically ill patients with normal platelet counts at ICU admission. To assess the prognostic relevance of declines in platelet counts occurring in the ICU, we conducted a database study in a large, prospective, multicenter, 4-year cohort of medical and surgical patients with ICU stays of ≥ 5 days.

Materials and Methods

The institutional review board of the French Society for Critical Care approved the inclusion of critically ill patients in the Outcomerea database. For this cohort study, we used data from patients included in the study over a 48-month period from nine ICUs, including four medical ICUs, two surgical ICUs, and three medical-surgical ICUs. Patients > 18 years of age who had been admitted to the ICU between April 1998 and April 2002 were included if their platelet counts at ICU admission were normal (ie, > 150 x 109 cells/L) and their ICU length of stay was ≥ 5 days. Briefly, each ICU chose one of the following two random patient selection methods: (1) consecutive admissions to specific ICU beds; or (2) consecutive ICU admissions in a specific month. Month or beds were allocated once a year by the database steering committee.

Data Collection and Baseline Data
Data were collected daily on computers by ICU physicians closely involved in establishing the database. All codes and definitions were written before data collection. For each patient, the ICU physician completed a case report form using data capture software (VIGIREATM; Outcomerea; Paris, France) then imported all records to the database. The following information was recorded prospectively: age and sex; underlying diseases using the McCabe score and Knaus classification8; ICU admission category (ie, medical, scheduled surgery, or unscheduled surgery); ICU admission diagnosis (ie, cardiac, respiratory, or neurologic failure; infection; and other), invasive procedures (ie, arterial or venous central catheter, Swan-Ganz catheter, and endotracheal intubation), and treatment of organ failures (ie, vasopressors, hemodialysis, and mechanical ventilation). The location of the patient prior to ICU admission was recorded, with transfer from wards defined as being in the same hospital or another hospital before ICU admission. Severity of illness was recorded at ICU admission and on each ICU day using the simplified acute physiology score II (SAPS II),9 the sequential organ failure assessment (SOFA) score,10 and the logistic organ dysfunction (LOD) score.11 Day 1 was defined as the interval from ICU admission to 8:00 AM on the next day; all other days were calendar days from 8:00 AM to 7:59 AM. Major bleeding was defined as blood transfusion requirements equal or greater than half the blood mass. Duration of stay in the ICU and an acute care hospital, and vital sign status at ICU and hospital discharge were recorded.

Quality of the Database
Quality was checked in 2003 by reviewing a random 2% sample of the data recorded in each ICU. This was done by intensivists from other ICUs. Interrater correlation coefficients ranged from 0.67 to 1 for clinical variables and for severity and organ-dysfunction scores; {kappa} coefficients for qualitative variables ranged from 0.5 to 0.9.

Statistical Analysis
Results are expressed as numerical values (percentage) for categoric variables, and median (interquartile range [IQR]) for continuous variables, unless stated otherwise. Comparisons were based on the Fisher exact test or {chi}2 test for categoric data, and on the Kruskal-Wallis test for continuous data. The relationships between hospital mortality and the study variables were evaluated using a logistic regression model in which vital status at hospital discharge was the variable of interest. Variables with p < 0.20 by univariate analysis were entered into the logistic regression model, where they were kept if the p value in the multivariable context was < 0.05 (stepwise variable-selection method). Relevant two-constituent interactions were proposed to the model. Interactions and the p values of their constituent term variables had to be < 0.05 to be kept in the model. Continuous variables were proposed to the model as native when they verified the log-linearity assumption; otherwise, they were converted and entered as dummy variables. We tested six thresholds of platelet-count declines on day 4, ranging from –10 to –60% of the baseline platelet count, by 10% decrements. Using day-4 platelet-count declines ensured that we had sufficient information from the previous days. The cutoff for the platelet-count decline was determined by receiver operating characteristic (ROC) analysis. All statistical tests were two-tailed, and p values < 0.05 were considered to be significant. The statistical analysis was performed using a statistical software package (SAS, version 8.0; SAS Institute; Cary, NC).

Results

Of the 3,500 patients admitted to the participating ICUs over the study period, 1,103 met our inclusion criteria. Of these patients, the 1,077 patients with no missing data form the basis for this study. The median length of the hospital stay was 32 days (IQR, 18 to 58 days). Table 1 shows patient characteristics. Patients who died before hospital discharge were older and more likely to have comorbidities, poor chronic health status, transfer from another hospital, and ICU admission for emergency surgery. These patients also had greater disease severity at ICU admission, as reflected by their worse SAPS II and LOD scores at ICU admission, and worse SOFA scores at ICU admission and on day 3.


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  		Table 1. Patient Characteristics*

 
All patients had normal platelet counts at ICU admission (Table 2 ), since this was an inclusion criterion. Although the median platelet count was normal over the first 10 days, declines in platelet counts were common. On the receiver operating characteristic curve (data not shown), the platelet-count decline associated with the best discrimination between survivors and nonsurvivors was 28.3%. We rounded this cutoff value to 30% to facilitate interpretation. Platelet counts at or above the cutoff occurred in 20.9% of survivors and 37.6% of nonsurvivors. Heparin treatment from ICU admission to day 4 was not associated with reaching the cutoff. Major bleeding occurred in 16 patients with platelet-count declines (5.7%) and 11 patients without platelet-count declines (1.4%) at or above the cutoff (p = 0.001). GI bleeding occurred in 27 patients who did reach the cutoff (9.6%) and 33 patients who did not reach the cutoff (4.1%; p = 0.0009). Platelet counts decreased moderately over the first 3 days in the ICU (Fig 1 ), reaching a nadir that was significantly lower in nonsurvivors than in survivors (198 and 227 x 109 cells/L, respectively; p = 0.001) then increased gradually starting on day 4 in survivors, and on day 6 in nonsurvivors. Kaplan-Meier estimates of hospital survival according to the size of the platelet-count decline showed that larger declines were associated with higher in-hospital mortality (Fig 2 ). Length of ICU stay did not influence the prognostic impact of a decline in platelet count of ≥ 30%.


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  		Table 2. Prognostic Significance of Declines in Platelet Counts (Univariate Analysis)*

 

Figure 1
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  		Figure 1. Platelet counts in the overall population (white bars), nonsurvivors (gray bars), and survivors (black bars) over the first 10 ICU days.

 

Figure 2
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  		Figure 2. Kaplan-Meier estimates of hospital survival according to the magnitude of the decline in platelet count on day 4.

 
During the ICU stay, 301 patients (27.9%) died. In addition, 30 patients died while in a ward after ICU discharge, yielding an in-hospital mortality rate of 30.7%. By univariate analysis, declines in platelet counts of between 10% and 60% on day 4 were significantly associated with hospital death, although platelet counts remained within the normal range in most patients (Table 2). Major bleeding occurred in 14 patients who survived (1.9%) and 13 patients who died (3.9%; p = 0.05). GI bleeding occurred in 28 patients who survived (3.8%) and in 32 patients who died (9.6%; p < 0.0001).

The following eight independent predictors of hospital mortality were identified (Table 3 ): a decline of ≥ 30% in platelet count on day 4 vs baseline (odds ratio [OR], 1.54; 95% confidence interval [CI], 1.12 to 2.14; p = 0.008); SOFA score was increased or unchanged between ICU admission and day 4 (OR, 1.40; 95% CI, 1.04 to 1.84; p = 0.02); older age (OR, 1.03 per year; 95% CI, 1.02 to 1.04 per year; p < 0.0001); ICU admission for coma (OR, 1.95; 95% CI, 1.27 to 1.99; p = 0.002); male gender (OR, 1.37; 95% CI, 1.01 to 1.85; p = 0.04); worse SAPS II (OR, 1.04 per point; 95% CI, 1.03 to 1.05 per point; p < 0.0001); transfer from a ward (OR, 1.58; 95% CI, 1.17 to 2.13; p = 0.002); and chronic disease (OR, 1.82; 95% CI, 1.35 to 2.45; p < 0.0001). Absolute platelet counts at ICU admission, on day 4, or at any time during the first 10 days of the ICU stay were not independently associated with hospital mortality. We proposed relevant interactions crossing severity of illness with diagnosis, chronic illness, and age, but none of them was selected by the stepwise procedure.


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  		Table 3. Independent Predictors of Hospital Mortality*

 
Discussion

This is the largest study so far examining the prognostic significance of a decline in platelet counts after ICU admission in patients with normal baseline platelet counts. Our results indicate that a decline in platelet count, often without thrombocytopenia, adds significant prognostic information to the parameters used in the current ICU scoring systems.

Thrombocytopenia is common in ICU patients, among whom 20 to 40% have platelet counts lower than 150 x 109 cells/L at some point during the course of their acute critical illness.12 Thrombocytopenia was associated with longer ICU stays, a higher incidence of bleeding events, greater transfusion requirements, and higher mortality.34131415 Most studies of the prognostic impact of platelet counts focused on outcomes in patients with < 100 to 150 x 109 platelets/L during the ICU stay, and included patients with and without thrombocytopenia at ICU admission. Changes in platelet counts over time may carry greater prognostic significance than absolute counts in patients with acute critical illnesses.23

Thrombocytopenia and a decline in platelet count may reflect the same pathophysiologic disturbances, including sepsis, disseminated intravascular coagulation, vitamin deficiencies, macrophage activation, drug-induced toxicity, and unidentified factors.1617 Our data agree with previous ICU studies514 showing a nadir in platelet count on day 3 in survivors and on day 4 in nonsurvivors. Although bleeding events are uncommon in patients whose platelet counts remain within the normal range,18 declines in platelet counts without thrombocytopenia may hold prognostic significance. Few studies have examined the potential prognostic significance of declining platelet counts in ICU patients. In a two-center study3 of prospectively collected data from 329 ICU patients, a 50% decline in platelet count was strongly associated with mortality, independently from the absolute platelet count. In a smaller single-center study of medical ICU patients,13 a 30% decline in platelet count during the ICU stay was independently associated with mortality. Although interesting time-dependent parameters were assessed in that study, 25% of patients had thrombocytopenia at ICU admission. In our cohort, a 30% decline in platelet count from day 0 to 4 independently predicted hospital mortality in patients with normal platelet counts at ICU admission and ICU stays of ≥ 5 days. Thus, the mortality rate was significantly higher in patients whose platelet counts decreased by at least 30% but remained within the normal range.

Scoring systems for severity of illness should take into account data that reflect changes in clinical status during the ICU stay.1920 Change-sensitive scores may help intensivists to assess the prognosis after patients have been in the ICU several days, thereby improving treatment decisions.19 To be appropriate for use in scoring systems, parameters must be readily available, independent from underlying diseases, and strongly associated with mortality. A 30% decline in platelet count meets these requirements. Therefore, we believe that it should be incorporated into new prognostic scoring systems for ICU patients.

Our study has several limitations. First, it was not designed to determine the etiologies of platelet-count declines in ICU patients, which involve multiple factors.11621 Therefore, we cannot speculate on the pathophysiologic mechanisms underlying the increased mortality in patients with larger platelet-count declines. The most frequently reported cause of thrombocytopenia341321 or blunted platelet-count rise after surgery5 was sepsis-related disseminated intravascular coagulation, which was far more common than liver disease, hematologic disorders, massive transfusions, drug-induced thrombocytopenia, and immune-mediated thrombocytopenia. However, severe sepsis and septic shock were present in only about one fifth of patients in our study, and most of our patients had platelet counts within the normal range throughout their ICU stay. Thus, a platelet-count decline, regardless of the mechanism, may be a strong prognostic marker. Second, because we excluded patients with thrombocytopenia at ICU admission, our cohort was not representative of the overall ICU patient population. The advantage, however, is that we avoided bias due to the prognostic impact of baseline thrombocytopenia. Furthermore, we excluded patients who stayed < 5 days in the ICU, since our objective was to investigate changes in platelet count over time. Third, a Cox proportional hazards model could be considered instead of logistic regression for the multivariable analysis. However, the Cox model assumes noninformative censoring; that is, that survivors at ICU discharge have the same risk of death as patients remaining in the ICU, which results in biased estimates of risk factors.22

In summary, using a large database of critically ill medical and surgical patients who spent at least 5 days in the ICU and had normal platelet counts at ICU admission, we demonstrated that a 30% decline in platelet counts on day 4 strongly predicted hospital mortality. This study should serve as the foundation of future well-designed studies to evaluate the impact of platelet count declines on mortality and causality.

Appendix

Members of the Outcomerea Study Group
Scientific Committee
Jean-François Timsit (Hôpital Albert Michallon and Institut National de la Santé et de la Recherche Médicale U578, Grenoble, France); Pierre Moine (Surgical ICU, Denver, CO); Arnaud de Lassence (ICU, Hôpital Louis Mourier, Combes, France); Elie Azoulay (Medical ICU, Hôpital Saint Louis, Paris, France); Yves Cohen (ICU, Hôpital Avicenne, Bobigny, France); Maïté Garrouste-Orgeas (ICU Hôpital Saint-Joseph, Paris, France); Lilia Soufir (ICU, Hôpital Saint-Joseph, Paris, France); Jean-Ralph Zahar (Microbiology Department, Hôpital Necker, Paris, France); Christophe Adrie (ICU, Hôpital Delafontaine, Saint Denis, France); Adel Benali (Microbiology and Infectious Diseases, Hôpital Saint-Joseph, Paris, France); Christophe Clec’h (ICU, Hôpital Avicenne, Bobigny, France); and Jean Carlet (ICU, Hôpital Saint-Joseph, Paris, France).

Biostatistical and Informatics Expertise
Jean-Francois Timsit (Epidemiology Group, Institut National de la Santé et de la Recherche Médicale U578, Grenoble, France); Sylvie Chevret (Medical Computer Sciences and Biostatistics Department, Hôpital Saint-Louis, Paris, France); Corinne Alberti (Medical Computer Sciences and Biostatistics Department, Hôpital Robert Debré, Paris, France); Muriel Tafflet (Outcomerea, France); Aurélien Vesin (Outcomerea, France); Adrien Francais (Outcomerea, France); Frederik Lecorre (Supelec, France); and Didier Nakache (Conservatoire National des Arts et Métiers, Paris, France).

Investigators of the Outcomerea Database
Christophe Adrie (ICU, Hôpital Delafontaine, Saint Denis, France); Bernard Allaouchiche (Surgical ICU, Lyon, France); Caroline Bornstain (ICU, Hôpital de Montfermeil, France); Alexandre Boyer (ICU, Hôpital Pellegrin, Bordeaux, France); Antoine Caubel (ICU, Hôpital Saint-Joseph, Paris, France); Christine Cheval (Surgical ICU, Hôpital Saint-Joseph, Paris, France); Marie-Alliette Costa de Beauregard (Nephrology, Hôpital Tenon, Paris, France); Jean-Pierre Colin (ICU, Hôpital de Dourdan, Dourdan, France); Anne-Sylvie Dumenil (Hôpital Antoine Béclère, Clamart France); Adrien Descorps-Declere (Hôpital Antoine Béclère, Clamart France); Jean-Philippe Fosse (ICU, Hôpital Avicenne, Bobigny, France); Samir Jamali (ICU, Hôpital de Dourdan, Dourdan, France); Christian Laplace (ICU, Hôpital Kremlin-Bicêtre, Bicêtre, France); Thierry Lazard (ICU, Hôpital de la Croix Saint-Simon, Paris, France); Eric Le Miere (ICU, Hôpital Louis Mourier, Combes, France); LAurent Montesino (ICU, Hôpital Bichat, Paris, France); Bruno Mourvillier (ICU, Hôpital Bichat, Paris, France); Benoît Misset (ICU, Hôpital Saint-Joseph, Paris, France); Delphine Moreau (ICU, Hôpital Saint-Louis, Paris, France); Etienne Pigné (ICU, Hôpital Louis Mourier, Combes, France); Carole Schwebel (Centre Hospitalier De L’université A Michallon, Grenoble, France); Gilles Troché (Hôpital Antoine, Béclère, Clamart France); Marie Thuong (ICU, Hôpital Delafontaine, Saint Denis, France); Guillaume Thierry (ICU, Hôpital Saint-Louis, Paris, France); Dany Toledano (Centre Hospitalier Gonnesse, France); Eric Vantalon (Surgical ICU, Hôpital Saint-Joseph, Paris, France); and François Vincent (ICU, Hôpital Avicenne, Bobigny, France).

Acknowledgements

This article is dedicated to Dr. Arnaud de Lassence, who died recently. Dr. de Lassence was a founding member of the Outcomerea study group, and a unique friend, colleague, and researcher.

Footnotes

Abbreviations: CI = confidence interval; IQR = interquartile range; LOD = logistic organ dysfunction; OR = odds ratio; SAPS II = simplified acute physiology score II; SOFA = sequential organ failure assessment

The Outcomerea study is supported by nonexclusive educational grants from the Centre National de Recherche Scientifique (Paris, France) and the Agence National de Valorisation de la Recherche, France.

The authors have no conflicts of interest to disclose.

Received for publication September 9, 2006. Accepted for publication March 21, 2007.

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