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Enrollment stalled for CAR T-cell study
Update: The hold on this trial has been lifted. Click here for additional details.
Memorial Sloan-Kettering Cancer Center has temporarily suspended enrollment in a study of chimeric antigen receptor (CAR) T-cell therapy, due to 2 patient deaths.
The study is an evaluation of CD19-targeted CAR T cells in patients with B-cell acute lymphoblastic leukemia (ALL).
Of the 22 patients enrolled on the study to date, 10 have died. But only 2 of these deaths gave researchers pause and made them question enrollment criteria.
Six deaths were a result of disease relapse or progression, and 2 patients died of complications from stem cell transplant.
The 2 deaths that prompted the suspension of enrollment occurred within 2 weeks of the patients receiving CAR T cells.
“The first of these patients had a prior history of cardiac disease, while the second patient died due to complications associated with persistent seizure activity,” said Renier Brentjens, MD, PhD, of Memorial Sloan-Kettering in New York.
“As a matter of routine review at Sloan-Kettering for adverse events on-study, our center made the decision to pause enrollment and review these 2 patients in greater detail.”
“And as a consequence of this review, we’ve amended the enrollment criteria in regards to comorbidities, thereby excluding patients with cardiac disease, and adjusted the T-cell dose based on the extent of disease, [in the] hope that this modification will reduce the cytokine release syndrome that these patients with morphological disease have experienced.”
The researchers expect the trial to resume enrollment soon.
Some results from this study were recently published in Science Translational Medicine, and Dr Brentjens presented the latest results at the AACR Annual Meeting 2014 in San Diego (abstract CT102*).
Thus far, the researchers have enrolled 22 adult patients who had relapsed or refractory B-ALL, were minimal residual disease-positive, or were in the first complete remission (CR1) at enrollment. Patients in CR1 were monitored and only received CAR T cells if they relapsed.
The remaining patients received re-induction chemotherapy (physician’s choice), followed by CAR T-cell infusion. After treatment, the options were allogeneic transplant, a different salvage therapy, or monitoring.
In all, 20 patients received a CAR T-cell dose of 3 x 106 T cells/kg. Eighty-two percent of patients initially achieved a CR, and 72% had a morphologic CR. The average time to CR was about 24.5 days.
Twelve of the responders were eligible for transplant. Of the 8 patients who ultimately underwent transplant and survived, 1 relapsed, but the rest remain in remission.
Dr Brentjens noted that some patients developed cytokine release syndrome, and this was related to the amount of disease present at the time of CAR T-cell infusion.
“Those patients that had only minimal residual disease at the time of CAR T-cell infusion . . . less than 5% blasts, generally had either no fever or very transient, low-grade fever,” he said.
“In contrast, all those patients that had morphologic residual disease at the time of CAR T-cell infusion demonstrated a high, persistent spike in fevers . . . , became hypotensive, and required transfer—for additional, closer monitoring—to our ICU.”
The researchers initially treated these patients with high-dose steroids, which reduced cytokine levels in the serum and ameliorated fevers. But it also rapidly reduced T-cell populations to undetectable levels.
Fortunately, another group of researchers subsequently discovered that the monoclonal antibody tocilizumab can treat cytokine release syndrome without inducing this side effect. So Dr Brentjens and his colleagues began using this drug and found it both safe and effective. 
*Information in the abstract differs from that presented at the meeting.
Update: The hold on this trial has been lifted. Click here for additional details.
Memorial Sloan-Kettering Cancer Center has temporarily suspended enrollment in a study of chimeric antigen receptor (CAR) T-cell therapy, due to 2 patient deaths.
The study is an evaluation of CD19-targeted CAR T cells in patients with B-cell acute lymphoblastic leukemia (ALL).
Of the 22 patients enrolled on the study to date, 10 have died. But only 2 of these deaths gave researchers pause and made them question enrollment criteria.
Six deaths were a result of disease relapse or progression, and 2 patients died of complications from stem cell transplant.
The 2 deaths that prompted the suspension of enrollment occurred within 2 weeks of the patients receiving CAR T cells.
“The first of these patients had a prior history of cardiac disease, while the second patient died due to complications associated with persistent seizure activity,” said Renier Brentjens, MD, PhD, of Memorial Sloan-Kettering in New York.
“As a matter of routine review at Sloan-Kettering for adverse events on-study, our center made the decision to pause enrollment and review these 2 patients in greater detail.”
“And as a consequence of this review, we’ve amended the enrollment criteria in regards to comorbidities, thereby excluding patients with cardiac disease, and adjusted the T-cell dose based on the extent of disease, [in the] hope that this modification will reduce the cytokine release syndrome that these patients with morphological disease have experienced.”
The researchers expect the trial to resume enrollment soon.
Some results from this study were recently published in Science Translational Medicine, and Dr Brentjens presented the latest results at the AACR Annual Meeting 2014 in San Diego (abstract CT102*).
Thus far, the researchers have enrolled 22 adult patients who had relapsed or refractory B-ALL, were minimal residual disease-positive, or were in the first complete remission (CR1) at enrollment. Patients in CR1 were monitored and only received CAR T cells if they relapsed.
The remaining patients received re-induction chemotherapy (physician’s choice), followed by CAR T-cell infusion. After treatment, the options were allogeneic transplant, a different salvage therapy, or monitoring.
In all, 20 patients received a CAR T-cell dose of 3 x 106 T cells/kg. Eighty-two percent of patients initially achieved a CR, and 72% had a morphologic CR. The average time to CR was about 24.5 days.
Twelve of the responders were eligible for transplant. Of the 8 patients who ultimately underwent transplant and survived, 1 relapsed, but the rest remain in remission.
Dr Brentjens noted that some patients developed cytokine release syndrome, and this was related to the amount of disease present at the time of CAR T-cell infusion.
“Those patients that had only minimal residual disease at the time of CAR T-cell infusion . . . less than 5% blasts, generally had either no fever or very transient, low-grade fever,” he said.
“In contrast, all those patients that had morphologic residual disease at the time of CAR T-cell infusion demonstrated a high, persistent spike in fevers . . . , became hypotensive, and required transfer—for additional, closer monitoring—to our ICU.”
The researchers initially treated these patients with high-dose steroids, which reduced cytokine levels in the serum and ameliorated fevers. But it also rapidly reduced T-cell populations to undetectable levels.
Fortunately, another group of researchers subsequently discovered that the monoclonal antibody tocilizumab can treat cytokine release syndrome without inducing this side effect. So Dr Brentjens and his colleagues began using this drug and found it both safe and effective.
*Information in the abstract differs from that presented at the meeting.
Update: The hold on this trial has been lifted. Click here for additional details.
Memorial Sloan-Kettering Cancer Center has temporarily suspended enrollment in a study of chimeric antigen receptor (CAR) T-cell therapy, due to 2 patient deaths.
The study is an evaluation of CD19-targeted CAR T cells in patients with B-cell acute lymphoblastic leukemia (ALL).
Of the 22 patients enrolled on the study to date, 10 have died. But only 2 of these deaths gave researchers pause and made them question enrollment criteria.
Six deaths were a result of disease relapse or progression, and 2 patients died of complications from stem cell transplant.
The 2 deaths that prompted the suspension of enrollment occurred within 2 weeks of the patients receiving CAR T cells.
“The first of these patients had a prior history of cardiac disease, while the second patient died due to complications associated with persistent seizure activity,” said Renier Brentjens, MD, PhD, of Memorial Sloan-Kettering in New York.
“As a matter of routine review at Sloan-Kettering for adverse events on-study, our center made the decision to pause enrollment and review these 2 patients in greater detail.”
“And as a consequence of this review, we’ve amended the enrollment criteria in regards to comorbidities, thereby excluding patients with cardiac disease, and adjusted the T-cell dose based on the extent of disease, [in the] hope that this modification will reduce the cytokine release syndrome that these patients with morphological disease have experienced.”
The researchers expect the trial to resume enrollment soon.
Some results from this study were recently published in Science Translational Medicine, and Dr Brentjens presented the latest results at the AACR Annual Meeting 2014 in San Diego (abstract CT102*).
Thus far, the researchers have enrolled 22 adult patients who had relapsed or refractory B-ALL, were minimal residual disease-positive, or were in the first complete remission (CR1) at enrollment. Patients in CR1 were monitored and only received CAR T cells if they relapsed.
The remaining patients received re-induction chemotherapy (physician’s choice), followed by CAR T-cell infusion. After treatment, the options were allogeneic transplant, a different salvage therapy, or monitoring.
In all, 20 patients received a CAR T-cell dose of 3 x 106 T cells/kg. Eighty-two percent of patients initially achieved a CR, and 72% had a morphologic CR. The average time to CR was about 24.5 days.
Twelve of the responders were eligible for transplant. Of the 8 patients who ultimately underwent transplant and survived, 1 relapsed, but the rest remain in remission.
Dr Brentjens noted that some patients developed cytokine release syndrome, and this was related to the amount of disease present at the time of CAR T-cell infusion.
“Those patients that had only minimal residual disease at the time of CAR T-cell infusion . . . less than 5% blasts, generally had either no fever or very transient, low-grade fever,” he said.
“In contrast, all those patients that had morphologic residual disease at the time of CAR T-cell infusion demonstrated a high, persistent spike in fevers . . . , became hypotensive, and required transfer—for additional, closer monitoring—to our ICU.”
The researchers initially treated these patients with high-dose steroids, which reduced cytokine levels in the serum and ameliorated fevers. But it also rapidly reduced T-cell populations to undetectable levels.
Fortunately, another group of researchers subsequently discovered that the monoclonal antibody tocilizumab can treat cytokine release syndrome without inducing this side effect. So Dr Brentjens and his colleagues began using this drug and found it both safe and effective.
*Information in the abstract differs from that presented at the meeting.
New insight into TPO and platelet production
Credit: Walter and Eliza Hall
Institute of Medical Research
Investigators say they’ve determined how thrombopoietin (TPO) stimulates platelet production, and their findings may have implications for myeloproliferative neoplasms.
Researchers have long known that TPO is responsible for signaling cells in the bone marrow to produce platelets, but precisely which cells respond to TPO’s signals has been unclear.
Now, a group of investigators studying the TPO receptor Mpl have pinpointed those cells and made an unexpected discovery.
“Thrombopoietin did not directly stimulate the platelet’s ‘parent’ cells—the megakaryocytes—to make more platelets,” said study author Ashley Ng, PhD, of the Walter and Eliza Hall Institute of Medical Research in Victoria, Australia.
“Thrombopoietin signals actually acted on stem cells and progenitor cells, several generations back.”
Dr Ng and his colleagues reported these findings in PNAS.
The researchers had generated mice that express the Mpl receptor normally on stem and progenitor cells but lack expression on megakaryocytes and platelets. And these mice exhibited “profound” megakaryocytosis and thrombocytosis, as well as “remarkable” expansion of megakaryocyte-committed and multipotent progenitor cells.
“The progenitor and stem cells in the bone marrow began massively expanding and effectively turned the bone marrow into a megakaryocyte-making machine,” Dr Ng said.
Furthermore, although the progenitor cells showed signs of chronic TPO overstimulation, TPO levels were normal. This suggests that stem and progenitor cells expressing Mpl were responsible for TPO clearance, according to the investigators.
“Our findings support a theory whereby megakaryocytes and platelets control platelet numbers by ‘mopping up’ excess amounts of thrombopoietin in the bone marrow,” Dr Ng said. “In fact, we show this ‘mopping up’ action is absolutely essential in preventing blood disease where too many megakaryocytes and platelets are produced.”
So the researchers believe these findings will have implications for myeloproliferative neoplasms, particularly essential thrombocythemia.
“[P]revious studies have shown megakaryocytes and platelets in people with essential thrombocythemia have fewer Mpl receptors, which fits our model for excessive platelet production,” Dr Ng said.
To add support to their model, the investigators compared the progenitor cells responsible for overproducing megakaryocytes in their model to progenitor cells from patients with essential thrombocythemia. Both sets of cells showed a TPO stimulation signature.
“We think this study now provides a comprehensive model of how thrombopoietin controls platelet production,” Dr Ng said, “and perhaps gives some insight into the biology and mechanism behind specific myeloproliferative disorders.”
Credit: Walter and Eliza Hall
Institute of Medical Research
Investigators say they’ve determined how thrombopoietin (TPO) stimulates platelet production, and their findings may have implications for myeloproliferative neoplasms.
Researchers have long known that TPO is responsible for signaling cells in the bone marrow to produce platelets, but precisely which cells respond to TPO’s signals has been unclear.
Now, a group of investigators studying the TPO receptor Mpl have pinpointed those cells and made an unexpected discovery.
“Thrombopoietin did not directly stimulate the platelet’s ‘parent’ cells—the megakaryocytes—to make more platelets,” said study author Ashley Ng, PhD, of the Walter and Eliza Hall Institute of Medical Research in Victoria, Australia.
“Thrombopoietin signals actually acted on stem cells and progenitor cells, several generations back.”
Dr Ng and his colleagues reported these findings in PNAS.
The researchers had generated mice that express the Mpl receptor normally on stem and progenitor cells but lack expression on megakaryocytes and platelets. And these mice exhibited “profound” megakaryocytosis and thrombocytosis, as well as “remarkable” expansion of megakaryocyte-committed and multipotent progenitor cells.
“The progenitor and stem cells in the bone marrow began massively expanding and effectively turned the bone marrow into a megakaryocyte-making machine,” Dr Ng said.
Furthermore, although the progenitor cells showed signs of chronic TPO overstimulation, TPO levels were normal. This suggests that stem and progenitor cells expressing Mpl were responsible for TPO clearance, according to the investigators.
“Our findings support a theory whereby megakaryocytes and platelets control platelet numbers by ‘mopping up’ excess amounts of thrombopoietin in the bone marrow,” Dr Ng said. “In fact, we show this ‘mopping up’ action is absolutely essential in preventing blood disease where too many megakaryocytes and platelets are produced.”
So the researchers believe these findings will have implications for myeloproliferative neoplasms, particularly essential thrombocythemia.
“[P]revious studies have shown megakaryocytes and platelets in people with essential thrombocythemia have fewer Mpl receptors, which fits our model for excessive platelet production,” Dr Ng said.
To add support to their model, the investigators compared the progenitor cells responsible for overproducing megakaryocytes in their model to progenitor cells from patients with essential thrombocythemia. Both sets of cells showed a TPO stimulation signature.
“We think this study now provides a comprehensive model of how thrombopoietin controls platelet production,” Dr Ng said, “and perhaps gives some insight into the biology and mechanism behind specific myeloproliferative disorders.”
Credit: Walter and Eliza Hall
Institute of Medical Research
Investigators say they’ve determined how thrombopoietin (TPO) stimulates platelet production, and their findings may have implications for myeloproliferative neoplasms.
Researchers have long known that TPO is responsible for signaling cells in the bone marrow to produce platelets, but precisely which cells respond to TPO’s signals has been unclear.
Now, a group of investigators studying the TPO receptor Mpl have pinpointed those cells and made an unexpected discovery.
“Thrombopoietin did not directly stimulate the platelet’s ‘parent’ cells—the megakaryocytes—to make more platelets,” said study author Ashley Ng, PhD, of the Walter and Eliza Hall Institute of Medical Research in Victoria, Australia.
“Thrombopoietin signals actually acted on stem cells and progenitor cells, several generations back.”
Dr Ng and his colleagues reported these findings in PNAS.
The researchers had generated mice that express the Mpl receptor normally on stem and progenitor cells but lack expression on megakaryocytes and platelets. And these mice exhibited “profound” megakaryocytosis and thrombocytosis, as well as “remarkable” expansion of megakaryocyte-committed and multipotent progenitor cells.
“The progenitor and stem cells in the bone marrow began massively expanding and effectively turned the bone marrow into a megakaryocyte-making machine,” Dr Ng said.
Furthermore, although the progenitor cells showed signs of chronic TPO overstimulation, TPO levels were normal. This suggests that stem and progenitor cells expressing Mpl were responsible for TPO clearance, according to the investigators.
“Our findings support a theory whereby megakaryocytes and platelets control platelet numbers by ‘mopping up’ excess amounts of thrombopoietin in the bone marrow,” Dr Ng said. “In fact, we show this ‘mopping up’ action is absolutely essential in preventing blood disease where too many megakaryocytes and platelets are produced.”
So the researchers believe these findings will have implications for myeloproliferative neoplasms, particularly essential thrombocythemia.
“[P]revious studies have shown megakaryocytes and platelets in people with essential thrombocythemia have fewer Mpl receptors, which fits our model for excessive platelet production,” Dr Ng said.
To add support to their model, the investigators compared the progenitor cells responsible for overproducing megakaryocytes in their model to progenitor cells from patients with essential thrombocythemia. Both sets of cells showed a TPO stimulation signature.
“We think this study now provides a comprehensive model of how thrombopoietin controls platelet production,” Dr Ng said, “and perhaps gives some insight into the biology and mechanism behind specific myeloproliferative disorders.”
Team creates 3D model of malaria parasite genome
a red blood cell; Credit: St Jude
Children’s Research Hospital
Scientists have generated a 3D model of the human malaria parasite genome at 3 different stages in the parasite’s life cycle, according to a report in Genome Research.
The team said this model of Plasmodium falciparum is the first to be generated during the progression of a parasite’s life cycle.
“We successfully mapped all physical interactions between genetic elements in the parasite nucleus,” said study author Karine Le Roch, PhD, of the University of California, Riverside.
“To do so, we used a chromosome conformation capture method, followed by high-throughput sequencing technology—a recently developed methodology to analyze the organization of chromosomes in the natural state of the cell. We then used the maps of all physical interactions to generate a 3D model of the genome for each stage of the parasite life cycle analyzed.”
The model revealed that genes that need to be highly expressed in the malaria parasite—for example, genes involved in translation—tend to cluster in the same area of the cell nucleus, while genes that need to be tightly repressed—for example, genes involved in virulence—are found elsewhere in the 3D structure in a “repression center.”
The 3D structure had one major repression center. And the researchers found that virulence genes, which are all organized into that one repression center in a distinct area in the nucleus, seem to drive the full genome organization of the parasite.
“If we understand how the malaria parasite genome is organized in the nucleus and which components control this organization, we may be able to disrupt this architecture and disrupt, too, the parasite development,” Dr Le Roch said.
“We know that the genome architecture is critical in regulating gene expression and, more important, in regulating genes that are critical for parasite virulence. Now, we can more carefully search for components or drugs that can disrupt this organization, helping in the identification of new antimalaria strategies.”
Dr Le Roch’s lab is now looking at other stages of the malaria life cycle in order to identify components responsible for the 3D genome architecture.
“The importance of the genome architecture was initially thought to be critical for only higher eukaryotes,” she explained. “But we found, to our surprise, that the genome architecture is closely linked to virulence, even in the case of the malaria parasite.”
a red blood cell; Credit: St Jude
Children’s Research Hospital
Scientists have generated a 3D model of the human malaria parasite genome at 3 different stages in the parasite’s life cycle, according to a report in Genome Research.
The team said this model of Plasmodium falciparum is the first to be generated during the progression of a parasite’s life cycle.
“We successfully mapped all physical interactions between genetic elements in the parasite nucleus,” said study author Karine Le Roch, PhD, of the University of California, Riverside.
“To do so, we used a chromosome conformation capture method, followed by high-throughput sequencing technology—a recently developed methodology to analyze the organization of chromosomes in the natural state of the cell. We then used the maps of all physical interactions to generate a 3D model of the genome for each stage of the parasite life cycle analyzed.”
The model revealed that genes that need to be highly expressed in the malaria parasite—for example, genes involved in translation—tend to cluster in the same area of the cell nucleus, while genes that need to be tightly repressed—for example, genes involved in virulence—are found elsewhere in the 3D structure in a “repression center.”
The 3D structure had one major repression center. And the researchers found that virulence genes, which are all organized into that one repression center in a distinct area in the nucleus, seem to drive the full genome organization of the parasite.
“If we understand how the malaria parasite genome is organized in the nucleus and which components control this organization, we may be able to disrupt this architecture and disrupt, too, the parasite development,” Dr Le Roch said.
“We know that the genome architecture is critical in regulating gene expression and, more important, in regulating genes that are critical for parasite virulence. Now, we can more carefully search for components or drugs that can disrupt this organization, helping in the identification of new antimalaria strategies.”
Dr Le Roch’s lab is now looking at other stages of the malaria life cycle in order to identify components responsible for the 3D genome architecture.
“The importance of the genome architecture was initially thought to be critical for only higher eukaryotes,” she explained. “But we found, to our surprise, that the genome architecture is closely linked to virulence, even in the case of the malaria parasite.”
a red blood cell; Credit: St Jude
Children’s Research Hospital
Scientists have generated a 3D model of the human malaria parasite genome at 3 different stages in the parasite’s life cycle, according to a report in Genome Research.
The team said this model of Plasmodium falciparum is the first to be generated during the progression of a parasite’s life cycle.
“We successfully mapped all physical interactions between genetic elements in the parasite nucleus,” said study author Karine Le Roch, PhD, of the University of California, Riverside.
“To do so, we used a chromosome conformation capture method, followed by high-throughput sequencing technology—a recently developed methodology to analyze the organization of chromosomes in the natural state of the cell. We then used the maps of all physical interactions to generate a 3D model of the genome for each stage of the parasite life cycle analyzed.”
The model revealed that genes that need to be highly expressed in the malaria parasite—for example, genes involved in translation—tend to cluster in the same area of the cell nucleus, while genes that need to be tightly repressed—for example, genes involved in virulence—are found elsewhere in the 3D structure in a “repression center.”
The 3D structure had one major repression center. And the researchers found that virulence genes, which are all organized into that one repression center in a distinct area in the nucleus, seem to drive the full genome organization of the parasite.
“If we understand how the malaria parasite genome is organized in the nucleus and which components control this organization, we may be able to disrupt this architecture and disrupt, too, the parasite development,” Dr Le Roch said.
“We know that the genome architecture is critical in regulating gene expression and, more important, in regulating genes that are critical for parasite virulence. Now, we can more carefully search for components or drugs that can disrupt this organization, helping in the identification of new antimalaria strategies.”
Dr Le Roch’s lab is now looking at other stages of the malaria life cycle in order to identify components responsible for the 3D genome architecture.
“The importance of the genome architecture was initially thought to be critical for only higher eukaryotes,” she explained. “But we found, to our surprise, that the genome architecture is closely linked to virulence, even in the case of the malaria parasite.”
Good or bad, immune responses to cancer are similar
Credit: Aaron Logan
SAN DIEGO—Researchers have found evidence to suggest there may be little difference between an immune response that kills cancer cells and one that stimulates tumor growth.
The team set out to determine whether the immune responses that mediate cancer immunosurveillance and those responsible for inflammatory facilitation are qualitatively or quantitatively distinct.
They tested antibodies in mouse models of a few different cancers, including rituximab in Burkitt lymphoma.
And they found that lower antibody concentrations stimulated tumor growth, while higher concentrations inhibited growth, and the dose range was “surprisingly narrow.”
The researchers reported these findings in a paper published in PNAS and a poster presentation at the AACR Annual Meeting 2014 (abstract 1063).
“We have found that the intensity difference between an immune response that stimulates cancer and one that kills it may not be very much,” said principal investigator Ajit Varki, MD, of the University of California, San Diego School of Medicine.
“This may come as a surprise to researchers exploring two areas typically considered distinct: the role of the immune system in preventing and killing cancers and the role of chronic inflammation in stimulating cancers. As always, it turns out that the immune system is a double-edged sword.”
The concept of naturally occurring immunosurveillance against malignancies is not new, and there is compelling evidence for it. But understanding this process is confounded by the fact that some types of immune reaction promote tumor development.
Dr Varki and his colleagues looked specifically at a non-human sialic acid sugar molecule called Neu5Gc. Previous research showed that Neu5Gc accumulates in human tumors from dietary sources, despite an ongoing antibody response against it.
The researchers deployed antibodies against Neu5Gc in a mouse tumor model to determine whether and to what degree the antibodies altered tumor progression. The team found that low antibody doses stimulated growth, but high doses inhibited it.
The effect occurred over a “linear and remarkably narrow range,” according to Dr Varki, generating an immune response curve or “inverse hormesis.” Moreover, this curve could be shifted to the left or right simply by modifying the quality of the immune response.
The researchers uncovered similar findings in experiments with mouse models of colon and lung cancer, as well as when they used rituximab in a model of Burkitt lymphoma.
Dr Varki said these results could have implications for all aspects of cancer research. The immune response can have multiple roles in the genesis of cancers, in altering the progress of established tumors and in anticancer therapies that use antibodies as drugs.
Dr Varki is a co-founder of the company Sialix, Inc., which has licensed UC San Diego technologies related to anti-Neu5Gc antibodies in cancer.
Credit: Aaron Logan
SAN DIEGO—Researchers have found evidence to suggest there may be little difference between an immune response that kills cancer cells and one that stimulates tumor growth.
The team set out to determine whether the immune responses that mediate cancer immunosurveillance and those responsible for inflammatory facilitation are qualitatively or quantitatively distinct.
They tested antibodies in mouse models of a few different cancers, including rituximab in Burkitt lymphoma.
And they found that lower antibody concentrations stimulated tumor growth, while higher concentrations inhibited growth, and the dose range was “surprisingly narrow.”
The researchers reported these findings in a paper published in PNAS and a poster presentation at the AACR Annual Meeting 2014 (abstract 1063).
“We have found that the intensity difference between an immune response that stimulates cancer and one that kills it may not be very much,” said principal investigator Ajit Varki, MD, of the University of California, San Diego School of Medicine.
“This may come as a surprise to researchers exploring two areas typically considered distinct: the role of the immune system in preventing and killing cancers and the role of chronic inflammation in stimulating cancers. As always, it turns out that the immune system is a double-edged sword.”
The concept of naturally occurring immunosurveillance against malignancies is not new, and there is compelling evidence for it. But understanding this process is confounded by the fact that some types of immune reaction promote tumor development.
Dr Varki and his colleagues looked specifically at a non-human sialic acid sugar molecule called Neu5Gc. Previous research showed that Neu5Gc accumulates in human tumors from dietary sources, despite an ongoing antibody response against it.
The researchers deployed antibodies against Neu5Gc in a mouse tumor model to determine whether and to what degree the antibodies altered tumor progression. The team found that low antibody doses stimulated growth, but high doses inhibited it.
The effect occurred over a “linear and remarkably narrow range,” according to Dr Varki, generating an immune response curve or “inverse hormesis.” Moreover, this curve could be shifted to the left or right simply by modifying the quality of the immune response.
The researchers uncovered similar findings in experiments with mouse models of colon and lung cancer, as well as when they used rituximab in a model of Burkitt lymphoma.
Dr Varki said these results could have implications for all aspects of cancer research. The immune response can have multiple roles in the genesis of cancers, in altering the progress of established tumors and in anticancer therapies that use antibodies as drugs.
Dr Varki is a co-founder of the company Sialix, Inc., which has licensed UC San Diego technologies related to anti-Neu5Gc antibodies in cancer.
Credit: Aaron Logan
SAN DIEGO—Researchers have found evidence to suggest there may be little difference between an immune response that kills cancer cells and one that stimulates tumor growth.
The team set out to determine whether the immune responses that mediate cancer immunosurveillance and those responsible for inflammatory facilitation are qualitatively or quantitatively distinct.
They tested antibodies in mouse models of a few different cancers, including rituximab in Burkitt lymphoma.
And they found that lower antibody concentrations stimulated tumor growth, while higher concentrations inhibited growth, and the dose range was “surprisingly narrow.”
The researchers reported these findings in a paper published in PNAS and a poster presentation at the AACR Annual Meeting 2014 (abstract 1063).
“We have found that the intensity difference between an immune response that stimulates cancer and one that kills it may not be very much,” said principal investigator Ajit Varki, MD, of the University of California, San Diego School of Medicine.
“This may come as a surprise to researchers exploring two areas typically considered distinct: the role of the immune system in preventing and killing cancers and the role of chronic inflammation in stimulating cancers. As always, it turns out that the immune system is a double-edged sword.”
The concept of naturally occurring immunosurveillance against malignancies is not new, and there is compelling evidence for it. But understanding this process is confounded by the fact that some types of immune reaction promote tumor development.
Dr Varki and his colleagues looked specifically at a non-human sialic acid sugar molecule called Neu5Gc. Previous research showed that Neu5Gc accumulates in human tumors from dietary sources, despite an ongoing antibody response against it.
The researchers deployed antibodies against Neu5Gc in a mouse tumor model to determine whether and to what degree the antibodies altered tumor progression. The team found that low antibody doses stimulated growth, but high doses inhibited it.
The effect occurred over a “linear and remarkably narrow range,” according to Dr Varki, generating an immune response curve or “inverse hormesis.” Moreover, this curve could be shifted to the left or right simply by modifying the quality of the immune response.
The researchers uncovered similar findings in experiments with mouse models of colon and lung cancer, as well as when they used rituximab in a model of Burkitt lymphoma.
Dr Varki said these results could have implications for all aspects of cancer research. The immune response can have multiple roles in the genesis of cancers, in altering the progress of established tumors and in anticancer therapies that use antibodies as drugs.
Dr Varki is a co-founder of the company Sialix, Inc., which has licensed UC San Diego technologies related to anti-Neu5Gc antibodies in cancer.
How autophagy helps cancer cells evade death
Credit: Sarah Pfau
SAN DIEGO—New research suggests that autophagy may allow cancer cells to recover and divide, rather than die, when faced with chemotherapy.
“What we showed is that if this mechanism doesn’t work right—for example, if autophagy is too high or if the target regulated by autophagy isn’t around—cancer cells may be able to rescue themselves from death caused by chemotherapies,” said study author Andrew Thorburn, PhD, of the University of Colorado Denver.
He and his colleagues believe this finding has important implications. It demonstrates a mechanism whereby autophagy controls cell death, and it further reinforces the clinical potential of inhibiting autophagy to sensitize cancer cells to chemotherapy.
Dr Thorburn and his colleagues recounted their research in Cell Reports and presented it in an education session at the AACR Annual Meeting 2014.
The researchers had set out to examine how autophagy affects canonical death receptor-induced mitochondrial outer membrane permeabilization (MOMP) and apoptosis. They found that MOMP occurs at variable times in cells, and it’s delayed by autophagy.
Furthermore, autophagy leads to inefficient MOMP. This causes some cells to die via a slower process than typical apoptosis, which allows them to eventually recover and divide.
Specifically, the researchers found that, as a cancer cell begins to die, mitochondrial cell walls break down. And the cell’s mitochondria release proteins via MOMP.
But then, high autophagy allows the cell to encapsulate and “digest” these released proteins before MOMP can keep the cell well and truly dead. The cell recovers and goes on to divide.
“The implication here is that if you inhibit autophagy, you’d make this less likely to happen; ie, when you kill cancer cells, they would stay dead,” Dr Thorburn said.
He and his colleagues also found that autophagy depends on the target PUMA to regulate cell death. When PUMA is absent, it doesn’t matter if autophagy is inhibited. Without the communicating action of PUMA, cancer cells evade apoptosis and continue to survive.
The researchers said this suggests autophagy can control apoptosis via a regulator that makes MOMP faster and more efficient, thus ensuring the rapid completion of apoptosis.
“Autophagy is complex and, as yet, not fully understood,” Dr Thorburn said. “But now that we see a molecular mechanism whereby cell fate can be determined by autophagy, we hope to discover patient populations that could benefit from drugs that inhibit this action.”
Credit: Sarah Pfau
SAN DIEGO—New research suggests that autophagy may allow cancer cells to recover and divide, rather than die, when faced with chemotherapy.
“What we showed is that if this mechanism doesn’t work right—for example, if autophagy is too high or if the target regulated by autophagy isn’t around—cancer cells may be able to rescue themselves from death caused by chemotherapies,” said study author Andrew Thorburn, PhD, of the University of Colorado Denver.
He and his colleagues believe this finding has important implications. It demonstrates a mechanism whereby autophagy controls cell death, and it further reinforces the clinical potential of inhibiting autophagy to sensitize cancer cells to chemotherapy.
Dr Thorburn and his colleagues recounted their research in Cell Reports and presented it in an education session at the AACR Annual Meeting 2014.
The researchers had set out to examine how autophagy affects canonical death receptor-induced mitochondrial outer membrane permeabilization (MOMP) and apoptosis. They found that MOMP occurs at variable times in cells, and it’s delayed by autophagy.
Furthermore, autophagy leads to inefficient MOMP. This causes some cells to die via a slower process than typical apoptosis, which allows them to eventually recover and divide.
Specifically, the researchers found that, as a cancer cell begins to die, mitochondrial cell walls break down. And the cell’s mitochondria release proteins via MOMP.
But then, high autophagy allows the cell to encapsulate and “digest” these released proteins before MOMP can keep the cell well and truly dead. The cell recovers and goes on to divide.
“The implication here is that if you inhibit autophagy, you’d make this less likely to happen; ie, when you kill cancer cells, they would stay dead,” Dr Thorburn said.
He and his colleagues also found that autophagy depends on the target PUMA to regulate cell death. When PUMA is absent, it doesn’t matter if autophagy is inhibited. Without the communicating action of PUMA, cancer cells evade apoptosis and continue to survive.
The researchers said this suggests autophagy can control apoptosis via a regulator that makes MOMP faster and more efficient, thus ensuring the rapid completion of apoptosis.
“Autophagy is complex and, as yet, not fully understood,” Dr Thorburn said. “But now that we see a molecular mechanism whereby cell fate can be determined by autophagy, we hope to discover patient populations that could benefit from drugs that inhibit this action.”
Credit: Sarah Pfau
SAN DIEGO—New research suggests that autophagy may allow cancer cells to recover and divide, rather than die, when faced with chemotherapy.
“What we showed is that if this mechanism doesn’t work right—for example, if autophagy is too high or if the target regulated by autophagy isn’t around—cancer cells may be able to rescue themselves from death caused by chemotherapies,” said study author Andrew Thorburn, PhD, of the University of Colorado Denver.
He and his colleagues believe this finding has important implications. It demonstrates a mechanism whereby autophagy controls cell death, and it further reinforces the clinical potential of inhibiting autophagy to sensitize cancer cells to chemotherapy.
Dr Thorburn and his colleagues recounted their research in Cell Reports and presented it in an education session at the AACR Annual Meeting 2014.
The researchers had set out to examine how autophagy affects canonical death receptor-induced mitochondrial outer membrane permeabilization (MOMP) and apoptosis. They found that MOMP occurs at variable times in cells, and it’s delayed by autophagy.
Furthermore, autophagy leads to inefficient MOMP. This causes some cells to die via a slower process than typical apoptosis, which allows them to eventually recover and divide.
Specifically, the researchers found that, as a cancer cell begins to die, mitochondrial cell walls break down. And the cell’s mitochondria release proteins via MOMP.
But then, high autophagy allows the cell to encapsulate and “digest” these released proteins before MOMP can keep the cell well and truly dead. The cell recovers and goes on to divide.
“The implication here is that if you inhibit autophagy, you’d make this less likely to happen; ie, when you kill cancer cells, they would stay dead,” Dr Thorburn said.
He and his colleagues also found that autophagy depends on the target PUMA to regulate cell death. When PUMA is absent, it doesn’t matter if autophagy is inhibited. Without the communicating action of PUMA, cancer cells evade apoptosis and continue to survive.
The researchers said this suggests autophagy can control apoptosis via a regulator that makes MOMP faster and more efficient, thus ensuring the rapid completion of apoptosis.
“Autophagy is complex and, as yet, not fully understood,” Dr Thorburn said. “But now that we see a molecular mechanism whereby cell fate can be determined by autophagy, we hope to discover patient populations that could benefit from drugs that inhibit this action.”
Clinical Deterioration Alerts
Patients deemed suitable for care on a general hospital unit are not expected to deteriorate; however, triage systems are not perfect, and some patients on general nursing units do develop critical illness during their hospitalization. Fortunately, there is mounting evidence that deteriorating patients exhibit measurable pathologic changes that could possibly be used to identify them prior to significant adverse outcomes, such as cardiac arrest.[1, 2, 3] Given the evidence that unplanned intensive care unit (ICU) transfers of patients on general units result in worse outcomes than more controlled ICU admissions,[1, 4, 5, 6] it is logical to assume that earlier identification of a deteriorating patient could provide a window of opportunity to prevent adverse outcomes.
The most commonly proposed systematic solution to the problem of identifying and stabilizing deteriorating patients on general hospital units includes some combination of an early warning system (EWS) to detect the deterioration and a rapid response team (RRT) to deal with it.[7, 8, 9, 10] We previously demonstrated that a relatively simple hospital‐specific method for generating EWS alerts derived from the electronic medical record (EMR) database is capable of predicting clinical deterioration and the need for ICU transfer, as well as hospital mortality, in non‐ICU patients admitted to general inpatient medicine units.[11, 12, 13, 14] However, our data also showed that simply providing the EWS alerts to these nursing units did not result in any demonstrable improvement in patient outcomes.[14] Therefore, we set out to determine whether linking real‐time EWS alerts to an intervention and notification of the RRT for patient evaluation could improve the outcomes of patients cared for on general inpatient units.
METHODS
Study Location
The study was conducted on 8 adult inpatient medicine units of Barnes‐Jewish Hospital, a 1250‐bed academic medical center in St. Louis, MO (January 15, 2013May 9, 2013). Patient care on the inpatient medicine units is delivered by either attending hospitalist physicians or dedicated housestaff physicians under the supervision of an attending physician. Continuous electronic vital sign monitoring is not provided on these units. The study was approved by the Washington University School of Medicine Human Studies Committee, and informed consent was waived. This was a nonblinded study (
Patients and Procedures
Patients admitted to the 8 medicine units received usual care during the study except as noted below. Manually obtained vital signs, laboratory data, and pharmacy data inputted in real time into the EMR were continuously assessed. The EWS searched for the 36 input variables previously described[11, 14] from the EMR for all patients admitted to the 8 medicine units 24 hours per day and 7 days a week. Values for every continuous parameter were scaled so that all measurements lay in the interval (0, 1) and were normalized by the minimum and maximum of the parameter as previously described.[14] To capture the temporal effects in our data, we retained a sliding window of all the collected data points within the last 24 hours. We then subdivided these data into a series of 6 sequential buckets of 4 hours each. We excluded the 2 hours of data prior to ICU transfer in building the model (so the data were 26 hours to 2 hours prior to ICU transfer for ICU transfer patients, and the first 24 hours of admission for everyone else). Eligible patients were selected for study entry when they triggered an alert for clinical deterioration as determined by the EWS.[11, 14]
The EWS alert was implemented in an internally developed, Java‐based clinical decision support rules engine, which identified when new data relevant to the model were available in a real‐time central data repository. In a clinical application, it is important to capture unusual changes in vital‐sign data over time. Such changes may precede clinical deterioration by hours, providing a chance to intervene if detected early enough. In addition, not all readings in time‐series data should be treated equally; the value of some kinds of data may change depending on their age. For example, a patient's condition may be better reflected by a blood‐oxygenation reading collected 1 hour ago than a reading collected 12 hours ago. This is the rationale for our use of a sliding window of all collected data points within the last 24 hours performed on a real‐time basis to determine the alert status of the patient.[11, 14]
We applied various threshold cut points to convert the EWS alert predictions into binary values and compared the results against the actual ICU transfer outcome.[14] A threshold of 0.9760 for specificity was chosen to achieve a sensitivity of approximately 40%. These operating characteristics were chosen in turn to generate a manageable number of alerts per hospital nursing unit per day (estimated at 12 per nursing unit per day). At this cut point, the C statistic was 0.8834, with an overall accuracy of 0.9292. In other words, our EWS alert system is calibrated so that for every 1000 patient discharges per year from these 8 hospital units, there would be 75 patients generating an alert, of which 30 patients would be expected to have the study outcome (ie, clinical deterioration requiring ICU transfer).
Once patients on the study units were identified as at risk for clinical deterioration by the EWS, they were assigned by a computerized random number generator to the intervention group or the control group. The control group was managed according to the usual care provided on the medicine units. The EWS alerts generated for the control patients were electronically stored, but these alerts were not sent to the RRT nurse, instead they were hidden from all clinical staff. The intervention group had their EWS alerts sent real time to the nursing member of the hospital's RRT. The RRT is composed of a registered nurse, a second‐ or third‐year internal medicine resident, and a respiratory therapist. The RRT was introduced in 2009 for the study units involved in this investigation. For 2009, 2010, and 2011 the RRT nurse was pulled from the staff of 1 of the hospital's ICUs in a rotating manner to respond to calls to the RRT as they occurred. Starting in 2012, the RRT nurse was established as a dedicated position without other clinical responsibilities. The RRT nurse carries a hospital‐issued mobile phone, to which the automated alert messages were sent real time, and was instructed to respond to all EWS alerts within 20 minutes of their receipt.
The RRT nurse would initially evaluate the alerted intervention patients using the Modified Early Warning Score[15, 16] and make further clinical and triage decisions based on those criteria and discussions with the RRT physician or the patient's treating physicians. The RRT focused their interventions using an internally developed tool called the Four Ds (discuss goals of care, drugs needing to be administered, diagnostics needing to be performed, and damage control with the use of oxygen, intravenous fluids, ventilation, and blood products). Patients evaluated by the RRT could have their current level of care maintained, have the frequency of vital sign monitoring increased, be transferred to an ICU, or have a code blue called for emergent resuscitation. The RRT reviewed goals of care for all patients to determine the appropriateness of interventions, especially for patients near the end of life who did not desire intensive care interventions. Nursing staff on the hospital units could also make calls to the RRT for patient evaluation at any time based on their clinical assessments performed during routine nursing rounds.
The primary efficacy outcome was the need for ICU transfer. Secondary outcome measures were hospital mortality and hospital length of stay. Pertinent demographic, laboratory, and clinical data were gathered prospectively including age, gender, race, underlying comorbidities, and severity of illness assessed by the Charlson comorbidity score and Elixhauser comorbidities.[17, 18]
Statistical Analysis
We required a sample size of 514 patients (257 per group) to achieve 80% power at a 5% significance level, based on the superiority design, a baseline event rate for ICU transfer of 20.0%, and an absolute reduction of 8.0% (PS Power and Sample Size Calculations, version 3.0, Vanderbilt Biostatistics, Nashville, TN). Continuous variables were reported as means with standard deviations or medians with 25th and 75th percentiles according to their distribution. The Student t test was used when comparing normally distributed data, and the Mann‐Whitney U test was employed to analyze non‐normally distributed data (eg, hospital length of stay). Categorical data were expressed as frequency distributions, and the [2] test was used to determine if differences existed between groups. A P value <0.05 was regarded as statistically significant. An interim analysis was planned for the data safety monitoring board to evaluate patient safety after 50% of the patients were recruited. The primary analysis was by intention to treat. Analyses were performed using SPSS version 11.0 for Windows (SPSS, Inc., Chicago, IL).
Data Safety Monitoring Board
An independent data safety and monitoring board was convened to monitor the study and to review and approve protocol amendments by the steering committee.
RESULTS
Between January 15, 2013 and May 9, 2013, there were 4731 consecutive patients admitted to the 8 inpatient units and electronically screened as the base population for this investigation. Five hundred seventy‐one (12.1%) patients triggered an alert and were enrolled into the study (Figure 1). There were 286 patients assigned to the intervention group and 285 assigned to the control group. No patients were lost to follow‐up. Demographics, reason for hospital admission, and comorbidities of the 2 groups were similar (Table 1). The number of patients having a separate RRT call by the primary nursing team on the hospital units within 24 hours of generating an alert was greater for the intervention group but did not reach statistical significance (19.9% vs 16.5%; odds ratio: 1.260; 95% confidence interval [CI]: 0.8231.931). Table 2 provides the new diagnostic and therapeutic interventions initiated within 24 hours after a EWS alert was generated. Patients in the intervention group were significantly more likely to have their primary care team physician notified by an RRT nurse regarding medical condition issues and to have oximetry and telemetry started, whereas control patients were significantly more likely to have new antibiotic orders written within 24 hours of generating an alert.
| Variable | Intervention Group, n=286 | Control Group, n=285 | P Value |
|---|---|---|---|
| Age, y | 63.7 16.0 | 63.1 15.4 | 0.495 |
| Gender, n (%) | |||
| Male | 132 (46.2) | 140 (49.1) | 0.503 |
| Female | 154 (53.8) | 145 (50.9) | |
| Race, n (%) | |||
| Caucasian | 155 (54.2) | 154 (54.0) | 0.417 |
| African American | 105 (36.7) | 113 (39.6) | |
| Other | 26 (9.1) | 18 (6.3) | |
| Reason for hospital admission | |||
| Cardiac | 12 (4.2) | 15 (5.3) | 0.548 |
| Pulmonary | 64 (22.4) | 72 (25.3) | 0.418 |
| Underlying malignancy | 6 (2.1) | 3 (1.1) | 0.504 |
| Renal disease | 31 (10.8) | 22 (7.7) | 0.248 |
| Thromboembolism | 4 (1.4) | 5 (1.8) | 0.752 |
| Infection | 55 (19.2) | 50 (17.5) | 0.603 |
| Neurologic disease | 33 (11.5) | 22 (7.7) | 0.122 |
| Intra‐abdominal disease | 41 (14.3) | 47 (16.5) | 0.476 |
| Hematologic condition | 4 (1.4) | 5 (1.8) | 0.752 |
| Endocrine disorder | 12 (4.2) | 6 (2.1) | 0.153 |
| Source of hospital admission | |||
| Emergency department | 201 (70.3) | 203 (71.2) | 0.200 |
| Direct admission | 36 (12.6) | 46 (16.1) | |
| Hospital transfer | 49 (17.1) | 36 (12.6) | |
| Charlson score | 6.7 3.6 | 6.6 3.2 | 0.879 |
| Elixhauser comorbidities score | 7.4 3.5 | 7.5 3.4 | 0.839 |
| Variable | Intervention Group, n=286 | Control Group, n=285 | P Value |
|---|---|---|---|
| |||
| Medications, n (%) | |||
| Antibiotics | 92 (32.2) | 121 (42.5) | 0.011 |
| Antiarrhythmics | 48 (16.8) | 44 (15.4) | 0.662 |
| Anticoagulants | 83 (29.0) | 97 (34.0) | 0.197 |
| Diuretics/antihypertensives | 71 (24.8) | 55 (19.3) | 0.111 |
| Bronchodilators | 78 (27.3) | 73 (25.6) | 0.653 |
| Anticonvulsives | 26 (9.1) | 27 (9.5) | 0.875 |
| Sedatives/narcotics | 0 (0.0) | 1 (0.4) | 0.499 |
| Respiratory support, n (%) | |||
| Noninvasive ventilation | 17 (6.0) | 9 (3.1) | 0.106 |
| Escalated oxygen support | 12 (4.2) | 7 (2.5) | 0.247 |
| Enhanced vital signs, n (%) | 50 (17.5) | 47 (16.5) | 0.752 |
| Maintenance intravenous fluids, n (%) | 48 (16.8) | 41 (14.4) | 0.430 |
| Vasopressors, n (%) | 57 (19.9) | 61 (21.4) | 0.664 |
| Bolus intravenous fluids, n (%) | 7 (2.4) | 14 (4.9) | 0.118 |
| Telemetry, n (%) | 198 (69.2) | 176 (61.8) | 0.052 |
| Oximetry, n (%) | 20 (7.0) | 6 (2.1) | 0.005 |
| New intravenous access, n (%) | 26 (9.1) | 35 (12.3) | 0.217 |
| Primary care team physician called by RRT nurse, n (%) | 82 (28.7) | 56 (19.6) | 0.012 |
Fifty‐one patients (17.8%) randomly assigned to the intervention group required ICU transfer compared with 52 of 285 patients (18.2%) in the control group (odds ratio: 0.972; 95% CI: 0.6351.490; P=0.898) (Table 3). Twenty‐one patients (7.3%) randomly assigned to the intervention group expired during their hospitalization compared with 22 of 285 patients (7.7%) in the control group (odds ratio: 0.947; 95%CI: 0.5091.764; P=0.865). Hospital length of stay was 8.49.5 days (median, 4.5 days; interquartile range, 2.311.4 days) for patients randomized to the intervention group and 9.411.1 days (median, 5.3 days; interquartile range, 3.211.2 days) for patients randomized to the control group (P=0.038). The ICU length of stay was 4.86.6 days (median, 2.9 days; interquartile range, 1.76.5 days) for patients randomized to the intervention group and 5.86.4 days (median, 2.9 days; interquartile range, 1.57.4) for patients randomized to the control group (P=0.812).The number of patients requiring transfer to a nursing home or long‐term acute care hospital was similar for patients in the intervention and control groups (26.9% vs 26.3%; odds ratio: 1.032; 95% CI: 0.7121.495; P=0.870). Similarly, the number of patients requiring hospital readmission before 30 days and 180 days, respectively, was similar for the 2 treatment groups (Table 3). For the combined study population, the EWS alerts were triggered 94138 hours (median, 27 hours; interquartile range, 7132 hours) prior to ICU transfer and 250204 hours (median200 hours; interquartile range, 54347 hours) prior to hospital mortality. The number of RRT calls for the 8 medicine units studied progressively increased from the start of the RRT program in 2009 through 2013 (121 in 2009, 194 in 2010, 298 in 2011, 415 in 2012, 415 in 2013; P<0.001 for the trend).
| Outcome | Intervention Group, n=286 | Control Group, n=285 | P Value |
|---|---|---|---|
| |||
| ICU transfer, n (%) | 51 (17.8) | 52 (18.2) | 0.898 |
| All‐cause hospital mortality, n (%) | 21 (7.3) | 22 (7.7) | 0.865 |
| Transfer to nursing home or LTAC, n (%) | 77 (26.9) | 75 (26.3) | 0.870 |
| 30‐day readmission, n (%) | 53 (18.5) | 62 (21.8) | 0.337 |
| 180‐day readmission, n (%) | 124 (43.4) | 117 (41.1) | 0.577 |
| Hospital length of stay, d* | 8.49.5, 4.5 [2.311.4] | 9.411.1, 5.3 [3.211.2] | 0.038 |
| ICU length of stay, d* | 4.86.6, 2.9 [1.76.5] | 5.86.4, 2.9 [1.57.4] | 0.812 |
DISCUSSION
We demonstrated that a real‐time EWS alert sent to a RRT nurse was associated with a modest reduction in hospital length of stay, but similar rates of hospital mortality, ICU transfer, and subsequent need for placement in a long‐term care setting compared with usual care. We also found the number of RRT calls to have increased progressively from 2009 to the present on the study units examined.
Unplanned ICU transfers occurring as early as within 8 hours of hospitalization are relatively common and associated with increased mortality.[6] Bapoje et al. evaluated a total of 152 patients over 1 year who had unplanned ICU transfers.[19] The most common reason was worsening of the problem for which the patient was admitted (48%). Other investigators have also attempted to identify predictors for clinical deterioration resulting in unplanned ICU transfer that could be employed in an EWS.[20, 21] Organizations like the Institute for Healthcare Improvement have called for the development and routine implementation of EWSs to direct the activities of RRTs and improve outcomes.[22] However, a recent systematic review found that much of the evidence in support of EWSs and emergency response teams is of poor quality and lacking prospective randomized trials.[23]
Our earlier experience demonstrated that simply providing an alert to nursing units did not result in any demonstrable improvements in the outcomes of high‐risk patients identified by our EWS.[14] Previous investigations have also had difficulty in demonstrating consistent outcome improvements with the use of EWSs and RRTs.[24, 25, 26, 27, 28, 29, 30, 31, 32] As a result of mandates from quality improvement organizations, most US hospitals currently employ RRTs for emergent mobilization of resources when a clinically deteriorating patient is identified on a hospital ward.[33, 34] Linking RRT actions with a validated real‐time alert may represent a way of improving the overall effectiveness of such teams for monitoring general hospital units, short of having all hospitalized patients in units staffed and monitored to provide higher levels of supervision (eg, ICUs, step‐down units).[9, 35]
An alternative approach to preventing patient deterioration is to provide closer overall monitoring. This has been accomplished by employing nursing personnel to increase monitoring, or with the use of automated monitoring equipment. Bellomo et al. showed that the deployment of electronic automated vital sign monitors on general hospital units was associated with improved utilization of RRTs, increased patient survival, and decreased time for vital sign measurement and recording.[36] Laurens and Dwyer found that implementation of medical emergency teams (METs) to respond to predefined MET activation criteria as observed by hospital staff resulted in reduced hospital mortality and reduced need for ICU transfer.[37] However, other investigators have observed that imperfect implementation of nursing‐performed observational monitoring resulted in no demonstrable benefit, illustrating the limitations of this approach.[38] Our findings suggest that nursing care of patients on general hospital units may be enhanced with the use of an EWS alert sent to the RRT. This is supported by the observation that communications between the RRT and the primary care teams was greater as was the use of telemetry and oximetry in the intervention arm. Moreover, there appears to have been a learning effect for the nursing staff that occurred on our study units, as evidenced by the increased number of RRT calls that occurred between 2009 and 2013. This change in nursing practices on these units certainly made it more difficult for us to observe outcome differences in our current study with the prescribed intervention, reinforcing the notion that evaluating an already established practice is a difficult proposition.[39]
Our study has several important limitations. First, the EWS alert was developed and validated at Barnes‐Jewish Hospital.[11, 12, 13, 14] We cannot say whether this alert will perform similarly in another hospital. Second, the EWS alert only contains data from medical patients. Development and validation of EWS alerts for other hospitalized populations, including surgical and pediatric patients, are needed to make such systems more generalizable. Third, the primary clinical outcome employed for this trial was problematic. Transfer to an ICU may not be an optimal outcome variable, as it may be desirable to transfer alerted patients to an ICU, which can be perceived to represent a soft landing for such patients once an alert has been generated. A better measure could be 30‐day all‐cause mortality, which would not be subject to clinician biases. Finally, we could not specifically identify explanations for the greater use of antibiotics in the control group despite similar rates of infection for both study arms. Future studies should closely evaluate the ability of EWS alerts to alter specific therapies (eg, reduce antibiotic utilization).
In summary, we have demonstrated that an EWS alert linked to a RRT likely contributed to a modest reduction in hospital length of stay, but no reductions in hospital mortality and ICU transfer. These findings suggest that inpatient deterioration on general hospital units can be identified and linked to a specific intervention. Continued efforts are needed to identify and implement systems that will not only accurately identify high‐risk patients on general hospital units but also intervene to improve their outcomes. We are moving forward with the development of a 2‐tiered EWS utilizing both EMR data and real‐time streamed vital sign data, to determine if we can further improve the prediction of clinical deterioration and potentially intervene in a more clinically meaningful manner.
Acknowledgements
The authors thank Ann Doyle, BSN, Lisa Mayfield, BSN, and Darain Mitchell for their assistance in carrying out this research protocol; and William Shannon, PhD, from the Division of General Medical Sciences at Washington University, for statistical support.
Disclosures: This study was funded in part by the Barnes‐Jewish Hospital Foundation, the Chest Foundation of the American College of Chest Physicians, and by grant number UL1 RR024992 from the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH), and NIH Roadmap for Medical Research. Its contents are solely the responsibility of the authors and do not necessarily represent the official view of the NCRR or NIH. The steering committee was responsible for the study design, execution, analysis, and content of the article. The Barnes‐Jewish Hospital Foundation, the American College of Chest Physicians, and the Chest Foundation were not involved in the design, conduct, or analysis of the trial. The authors report no conflicts of interest. Marin Kollef, Yixin Chen, Kevin Heard, Gina LaRossa, Chenyang Lu, Nathan Martin, Nelda Martin, Scott Micek, and Thomas Bailey have all made substantial contributions to conception and design, or acquisition of data, or analysis and interpretation of data; have drafted the submitted article or revised it critically for important intellectual content; have provided final approval of the version to be published; and have agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
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- , , . Imperfect implementation of an early warning scoring system in a danish teaching hospital: a cross‐sectional study. PLoS One. 2013;8:e70068.
- , . Introduction of medical emergency teams in Australia and New Zealand: A multicentre study. Crit Care. 2008;12(3):151.
Patients deemed suitable for care on a general hospital unit are not expected to deteriorate; however, triage systems are not perfect, and some patients on general nursing units do develop critical illness during their hospitalization. Fortunately, there is mounting evidence that deteriorating patients exhibit measurable pathologic changes that could possibly be used to identify them prior to significant adverse outcomes, such as cardiac arrest.[1, 2, 3] Given the evidence that unplanned intensive care unit (ICU) transfers of patients on general units result in worse outcomes than more controlled ICU admissions,[1, 4, 5, 6] it is logical to assume that earlier identification of a deteriorating patient could provide a window of opportunity to prevent adverse outcomes.
The most commonly proposed systematic solution to the problem of identifying and stabilizing deteriorating patients on general hospital units includes some combination of an early warning system (EWS) to detect the deterioration and a rapid response team (RRT) to deal with it.[7, 8, 9, 10] We previously demonstrated that a relatively simple hospital‐specific method for generating EWS alerts derived from the electronic medical record (EMR) database is capable of predicting clinical deterioration and the need for ICU transfer, as well as hospital mortality, in non‐ICU patients admitted to general inpatient medicine units.[11, 12, 13, 14] However, our data also showed that simply providing the EWS alerts to these nursing units did not result in any demonstrable improvement in patient outcomes.[14] Therefore, we set out to determine whether linking real‐time EWS alerts to an intervention and notification of the RRT for patient evaluation could improve the outcomes of patients cared for on general inpatient units.
METHODS
Study Location
The study was conducted on 8 adult inpatient medicine units of Barnes‐Jewish Hospital, a 1250‐bed academic medical center in St. Louis, MO (January 15, 2013May 9, 2013). Patient care on the inpatient medicine units is delivered by either attending hospitalist physicians or dedicated housestaff physicians under the supervision of an attending physician. Continuous electronic vital sign monitoring is not provided on these units. The study was approved by the Washington University School of Medicine Human Studies Committee, and informed consent was waived. This was a nonblinded study (
Patients and Procedures
Patients admitted to the 8 medicine units received usual care during the study except as noted below. Manually obtained vital signs, laboratory data, and pharmacy data inputted in real time into the EMR were continuously assessed. The EWS searched for the 36 input variables previously described[11, 14] from the EMR for all patients admitted to the 8 medicine units 24 hours per day and 7 days a week. Values for every continuous parameter were scaled so that all measurements lay in the interval (0, 1) and were normalized by the minimum and maximum of the parameter as previously described.[14] To capture the temporal effects in our data, we retained a sliding window of all the collected data points within the last 24 hours. We then subdivided these data into a series of 6 sequential buckets of 4 hours each. We excluded the 2 hours of data prior to ICU transfer in building the model (so the data were 26 hours to 2 hours prior to ICU transfer for ICU transfer patients, and the first 24 hours of admission for everyone else). Eligible patients were selected for study entry when they triggered an alert for clinical deterioration as determined by the EWS.[11, 14]
The EWS alert was implemented in an internally developed, Java‐based clinical decision support rules engine, which identified when new data relevant to the model were available in a real‐time central data repository. In a clinical application, it is important to capture unusual changes in vital‐sign data over time. Such changes may precede clinical deterioration by hours, providing a chance to intervene if detected early enough. In addition, not all readings in time‐series data should be treated equally; the value of some kinds of data may change depending on their age. For example, a patient's condition may be better reflected by a blood‐oxygenation reading collected 1 hour ago than a reading collected 12 hours ago. This is the rationale for our use of a sliding window of all collected data points within the last 24 hours performed on a real‐time basis to determine the alert status of the patient.[11, 14]
We applied various threshold cut points to convert the EWS alert predictions into binary values and compared the results against the actual ICU transfer outcome.[14] A threshold of 0.9760 for specificity was chosen to achieve a sensitivity of approximately 40%. These operating characteristics were chosen in turn to generate a manageable number of alerts per hospital nursing unit per day (estimated at 12 per nursing unit per day). At this cut point, the C statistic was 0.8834, with an overall accuracy of 0.9292. In other words, our EWS alert system is calibrated so that for every 1000 patient discharges per year from these 8 hospital units, there would be 75 patients generating an alert, of which 30 patients would be expected to have the study outcome (ie, clinical deterioration requiring ICU transfer).
Once patients on the study units were identified as at risk for clinical deterioration by the EWS, they were assigned by a computerized random number generator to the intervention group or the control group. The control group was managed according to the usual care provided on the medicine units. The EWS alerts generated for the control patients were electronically stored, but these alerts were not sent to the RRT nurse, instead they were hidden from all clinical staff. The intervention group had their EWS alerts sent real time to the nursing member of the hospital's RRT. The RRT is composed of a registered nurse, a second‐ or third‐year internal medicine resident, and a respiratory therapist. The RRT was introduced in 2009 for the study units involved in this investigation. For 2009, 2010, and 2011 the RRT nurse was pulled from the staff of 1 of the hospital's ICUs in a rotating manner to respond to calls to the RRT as they occurred. Starting in 2012, the RRT nurse was established as a dedicated position without other clinical responsibilities. The RRT nurse carries a hospital‐issued mobile phone, to which the automated alert messages were sent real time, and was instructed to respond to all EWS alerts within 20 minutes of their receipt.
The RRT nurse would initially evaluate the alerted intervention patients using the Modified Early Warning Score[15, 16] and make further clinical and triage decisions based on those criteria and discussions with the RRT physician or the patient's treating physicians. The RRT focused their interventions using an internally developed tool called the Four Ds (discuss goals of care, drugs needing to be administered, diagnostics needing to be performed, and damage control with the use of oxygen, intravenous fluids, ventilation, and blood products). Patients evaluated by the RRT could have their current level of care maintained, have the frequency of vital sign monitoring increased, be transferred to an ICU, or have a code blue called for emergent resuscitation. The RRT reviewed goals of care for all patients to determine the appropriateness of interventions, especially for patients near the end of life who did not desire intensive care interventions. Nursing staff on the hospital units could also make calls to the RRT for patient evaluation at any time based on their clinical assessments performed during routine nursing rounds.
The primary efficacy outcome was the need for ICU transfer. Secondary outcome measures were hospital mortality and hospital length of stay. Pertinent demographic, laboratory, and clinical data were gathered prospectively including age, gender, race, underlying comorbidities, and severity of illness assessed by the Charlson comorbidity score and Elixhauser comorbidities.[17, 18]
Statistical Analysis
We required a sample size of 514 patients (257 per group) to achieve 80% power at a 5% significance level, based on the superiority design, a baseline event rate for ICU transfer of 20.0%, and an absolute reduction of 8.0% (PS Power and Sample Size Calculations, version 3.0, Vanderbilt Biostatistics, Nashville, TN). Continuous variables were reported as means with standard deviations or medians with 25th and 75th percentiles according to their distribution. The Student t test was used when comparing normally distributed data, and the Mann‐Whitney U test was employed to analyze non‐normally distributed data (eg, hospital length of stay). Categorical data were expressed as frequency distributions, and the [2] test was used to determine if differences existed between groups. A P value <0.05 was regarded as statistically significant. An interim analysis was planned for the data safety monitoring board to evaluate patient safety after 50% of the patients were recruited. The primary analysis was by intention to treat. Analyses were performed using SPSS version 11.0 for Windows (SPSS, Inc., Chicago, IL).
Data Safety Monitoring Board
An independent data safety and monitoring board was convened to monitor the study and to review and approve protocol amendments by the steering committee.
RESULTS
Between January 15, 2013 and May 9, 2013, there were 4731 consecutive patients admitted to the 8 inpatient units and electronically screened as the base population for this investigation. Five hundred seventy‐one (12.1%) patients triggered an alert and were enrolled into the study (Figure 1). There were 286 patients assigned to the intervention group and 285 assigned to the control group. No patients were lost to follow‐up. Demographics, reason for hospital admission, and comorbidities of the 2 groups were similar (Table 1). The number of patients having a separate RRT call by the primary nursing team on the hospital units within 24 hours of generating an alert was greater for the intervention group but did not reach statistical significance (19.9% vs 16.5%; odds ratio: 1.260; 95% confidence interval [CI]: 0.8231.931). Table 2 provides the new diagnostic and therapeutic interventions initiated within 24 hours after a EWS alert was generated. Patients in the intervention group were significantly more likely to have their primary care team physician notified by an RRT nurse regarding medical condition issues and to have oximetry and telemetry started, whereas control patients were significantly more likely to have new antibiotic orders written within 24 hours of generating an alert.
| Variable | Intervention Group, n=286 | Control Group, n=285 | P Value |
|---|---|---|---|
| Age, y | 63.7 16.0 | 63.1 15.4 | 0.495 |
| Gender, n (%) | |||
| Male | 132 (46.2) | 140 (49.1) | 0.503 |
| Female | 154 (53.8) | 145 (50.9) | |
| Race, n (%) | |||
| Caucasian | 155 (54.2) | 154 (54.0) | 0.417 |
| African American | 105 (36.7) | 113 (39.6) | |
| Other | 26 (9.1) | 18 (6.3) | |
| Reason for hospital admission | |||
| Cardiac | 12 (4.2) | 15 (5.3) | 0.548 |
| Pulmonary | 64 (22.4) | 72 (25.3) | 0.418 |
| Underlying malignancy | 6 (2.1) | 3 (1.1) | 0.504 |
| Renal disease | 31 (10.8) | 22 (7.7) | 0.248 |
| Thromboembolism | 4 (1.4) | 5 (1.8) | 0.752 |
| Infection | 55 (19.2) | 50 (17.5) | 0.603 |
| Neurologic disease | 33 (11.5) | 22 (7.7) | 0.122 |
| Intra‐abdominal disease | 41 (14.3) | 47 (16.5) | 0.476 |
| Hematologic condition | 4 (1.4) | 5 (1.8) | 0.752 |
| Endocrine disorder | 12 (4.2) | 6 (2.1) | 0.153 |
| Source of hospital admission | |||
| Emergency department | 201 (70.3) | 203 (71.2) | 0.200 |
| Direct admission | 36 (12.6) | 46 (16.1) | |
| Hospital transfer | 49 (17.1) | 36 (12.6) | |
| Charlson score | 6.7 3.6 | 6.6 3.2 | 0.879 |
| Elixhauser comorbidities score | 7.4 3.5 | 7.5 3.4 | 0.839 |
| Variable | Intervention Group, n=286 | Control Group, n=285 | P Value |
|---|---|---|---|
| |||
| Medications, n (%) | |||
| Antibiotics | 92 (32.2) | 121 (42.5) | 0.011 |
| Antiarrhythmics | 48 (16.8) | 44 (15.4) | 0.662 |
| Anticoagulants | 83 (29.0) | 97 (34.0) | 0.197 |
| Diuretics/antihypertensives | 71 (24.8) | 55 (19.3) | 0.111 |
| Bronchodilators | 78 (27.3) | 73 (25.6) | 0.653 |
| Anticonvulsives | 26 (9.1) | 27 (9.5) | 0.875 |
| Sedatives/narcotics | 0 (0.0) | 1 (0.4) | 0.499 |
| Respiratory support, n (%) | |||
| Noninvasive ventilation | 17 (6.0) | 9 (3.1) | 0.106 |
| Escalated oxygen support | 12 (4.2) | 7 (2.5) | 0.247 |
| Enhanced vital signs, n (%) | 50 (17.5) | 47 (16.5) | 0.752 |
| Maintenance intravenous fluids, n (%) | 48 (16.8) | 41 (14.4) | 0.430 |
| Vasopressors, n (%) | 57 (19.9) | 61 (21.4) | 0.664 |
| Bolus intravenous fluids, n (%) | 7 (2.4) | 14 (4.9) | 0.118 |
| Telemetry, n (%) | 198 (69.2) | 176 (61.8) | 0.052 |
| Oximetry, n (%) | 20 (7.0) | 6 (2.1) | 0.005 |
| New intravenous access, n (%) | 26 (9.1) | 35 (12.3) | 0.217 |
| Primary care team physician called by RRT nurse, n (%) | 82 (28.7) | 56 (19.6) | 0.012 |
Fifty‐one patients (17.8%) randomly assigned to the intervention group required ICU transfer compared with 52 of 285 patients (18.2%) in the control group (odds ratio: 0.972; 95% CI: 0.6351.490; P=0.898) (Table 3). Twenty‐one patients (7.3%) randomly assigned to the intervention group expired during their hospitalization compared with 22 of 285 patients (7.7%) in the control group (odds ratio: 0.947; 95%CI: 0.5091.764; P=0.865). Hospital length of stay was 8.49.5 days (median, 4.5 days; interquartile range, 2.311.4 days) for patients randomized to the intervention group and 9.411.1 days (median, 5.3 days; interquartile range, 3.211.2 days) for patients randomized to the control group (P=0.038). The ICU length of stay was 4.86.6 days (median, 2.9 days; interquartile range, 1.76.5 days) for patients randomized to the intervention group and 5.86.4 days (median, 2.9 days; interquartile range, 1.57.4) for patients randomized to the control group (P=0.812).The number of patients requiring transfer to a nursing home or long‐term acute care hospital was similar for patients in the intervention and control groups (26.9% vs 26.3%; odds ratio: 1.032; 95% CI: 0.7121.495; P=0.870). Similarly, the number of patients requiring hospital readmission before 30 days and 180 days, respectively, was similar for the 2 treatment groups (Table 3). For the combined study population, the EWS alerts were triggered 94138 hours (median, 27 hours; interquartile range, 7132 hours) prior to ICU transfer and 250204 hours (median200 hours; interquartile range, 54347 hours) prior to hospital mortality. The number of RRT calls for the 8 medicine units studied progressively increased from the start of the RRT program in 2009 through 2013 (121 in 2009, 194 in 2010, 298 in 2011, 415 in 2012, 415 in 2013; P<0.001 for the trend).
| Outcome | Intervention Group, n=286 | Control Group, n=285 | P Value |
|---|---|---|---|
| |||
| ICU transfer, n (%) | 51 (17.8) | 52 (18.2) | 0.898 |
| All‐cause hospital mortality, n (%) | 21 (7.3) | 22 (7.7) | 0.865 |
| Transfer to nursing home or LTAC, n (%) | 77 (26.9) | 75 (26.3) | 0.870 |
| 30‐day readmission, n (%) | 53 (18.5) | 62 (21.8) | 0.337 |
| 180‐day readmission, n (%) | 124 (43.4) | 117 (41.1) | 0.577 |
| Hospital length of stay, d* | 8.49.5, 4.5 [2.311.4] | 9.411.1, 5.3 [3.211.2] | 0.038 |
| ICU length of stay, d* | 4.86.6, 2.9 [1.76.5] | 5.86.4, 2.9 [1.57.4] | 0.812 |
DISCUSSION
We demonstrated that a real‐time EWS alert sent to a RRT nurse was associated with a modest reduction in hospital length of stay, but similar rates of hospital mortality, ICU transfer, and subsequent need for placement in a long‐term care setting compared with usual care. We also found the number of RRT calls to have increased progressively from 2009 to the present on the study units examined.
Unplanned ICU transfers occurring as early as within 8 hours of hospitalization are relatively common and associated with increased mortality.[6] Bapoje et al. evaluated a total of 152 patients over 1 year who had unplanned ICU transfers.[19] The most common reason was worsening of the problem for which the patient was admitted (48%). Other investigators have also attempted to identify predictors for clinical deterioration resulting in unplanned ICU transfer that could be employed in an EWS.[20, 21] Organizations like the Institute for Healthcare Improvement have called for the development and routine implementation of EWSs to direct the activities of RRTs and improve outcomes.[22] However, a recent systematic review found that much of the evidence in support of EWSs and emergency response teams is of poor quality and lacking prospective randomized trials.[23]
Our earlier experience demonstrated that simply providing an alert to nursing units did not result in any demonstrable improvements in the outcomes of high‐risk patients identified by our EWS.[14] Previous investigations have also had difficulty in demonstrating consistent outcome improvements with the use of EWSs and RRTs.[24, 25, 26, 27, 28, 29, 30, 31, 32] As a result of mandates from quality improvement organizations, most US hospitals currently employ RRTs for emergent mobilization of resources when a clinically deteriorating patient is identified on a hospital ward.[33, 34] Linking RRT actions with a validated real‐time alert may represent a way of improving the overall effectiveness of such teams for monitoring general hospital units, short of having all hospitalized patients in units staffed and monitored to provide higher levels of supervision (eg, ICUs, step‐down units).[9, 35]
An alternative approach to preventing patient deterioration is to provide closer overall monitoring. This has been accomplished by employing nursing personnel to increase monitoring, or with the use of automated monitoring equipment. Bellomo et al. showed that the deployment of electronic automated vital sign monitors on general hospital units was associated with improved utilization of RRTs, increased patient survival, and decreased time for vital sign measurement and recording.[36] Laurens and Dwyer found that implementation of medical emergency teams (METs) to respond to predefined MET activation criteria as observed by hospital staff resulted in reduced hospital mortality and reduced need for ICU transfer.[37] However, other investigators have observed that imperfect implementation of nursing‐performed observational monitoring resulted in no demonstrable benefit, illustrating the limitations of this approach.[38] Our findings suggest that nursing care of patients on general hospital units may be enhanced with the use of an EWS alert sent to the RRT. This is supported by the observation that communications between the RRT and the primary care teams was greater as was the use of telemetry and oximetry in the intervention arm. Moreover, there appears to have been a learning effect for the nursing staff that occurred on our study units, as evidenced by the increased number of RRT calls that occurred between 2009 and 2013. This change in nursing practices on these units certainly made it more difficult for us to observe outcome differences in our current study with the prescribed intervention, reinforcing the notion that evaluating an already established practice is a difficult proposition.[39]
Our study has several important limitations. First, the EWS alert was developed and validated at Barnes‐Jewish Hospital.[11, 12, 13, 14] We cannot say whether this alert will perform similarly in another hospital. Second, the EWS alert only contains data from medical patients. Development and validation of EWS alerts for other hospitalized populations, including surgical and pediatric patients, are needed to make such systems more generalizable. Third, the primary clinical outcome employed for this trial was problematic. Transfer to an ICU may not be an optimal outcome variable, as it may be desirable to transfer alerted patients to an ICU, which can be perceived to represent a soft landing for such patients once an alert has been generated. A better measure could be 30‐day all‐cause mortality, which would not be subject to clinician biases. Finally, we could not specifically identify explanations for the greater use of antibiotics in the control group despite similar rates of infection for both study arms. Future studies should closely evaluate the ability of EWS alerts to alter specific therapies (eg, reduce antibiotic utilization).
In summary, we have demonstrated that an EWS alert linked to a RRT likely contributed to a modest reduction in hospital length of stay, but no reductions in hospital mortality and ICU transfer. These findings suggest that inpatient deterioration on general hospital units can be identified and linked to a specific intervention. Continued efforts are needed to identify and implement systems that will not only accurately identify high‐risk patients on general hospital units but also intervene to improve their outcomes. We are moving forward with the development of a 2‐tiered EWS utilizing both EMR data and real‐time streamed vital sign data, to determine if we can further improve the prediction of clinical deterioration and potentially intervene in a more clinically meaningful manner.
Acknowledgements
The authors thank Ann Doyle, BSN, Lisa Mayfield, BSN, and Darain Mitchell for their assistance in carrying out this research protocol; and William Shannon, PhD, from the Division of General Medical Sciences at Washington University, for statistical support.
Disclosures: This study was funded in part by the Barnes‐Jewish Hospital Foundation, the Chest Foundation of the American College of Chest Physicians, and by grant number UL1 RR024992 from the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH), and NIH Roadmap for Medical Research. Its contents are solely the responsibility of the authors and do not necessarily represent the official view of the NCRR or NIH. The steering committee was responsible for the study design, execution, analysis, and content of the article. The Barnes‐Jewish Hospital Foundation, the American College of Chest Physicians, and the Chest Foundation were not involved in the design, conduct, or analysis of the trial. The authors report no conflicts of interest. Marin Kollef, Yixin Chen, Kevin Heard, Gina LaRossa, Chenyang Lu, Nathan Martin, Nelda Martin, Scott Micek, and Thomas Bailey have all made substantial contributions to conception and design, or acquisition of data, or analysis and interpretation of data; have drafted the submitted article or revised it critically for important intellectual content; have provided final approval of the version to be published; and have agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
Patients deemed suitable for care on a general hospital unit are not expected to deteriorate; however, triage systems are not perfect, and some patients on general nursing units do develop critical illness during their hospitalization. Fortunately, there is mounting evidence that deteriorating patients exhibit measurable pathologic changes that could possibly be used to identify them prior to significant adverse outcomes, such as cardiac arrest.[1, 2, 3] Given the evidence that unplanned intensive care unit (ICU) transfers of patients on general units result in worse outcomes than more controlled ICU admissions,[1, 4, 5, 6] it is logical to assume that earlier identification of a deteriorating patient could provide a window of opportunity to prevent adverse outcomes.
The most commonly proposed systematic solution to the problem of identifying and stabilizing deteriorating patients on general hospital units includes some combination of an early warning system (EWS) to detect the deterioration and a rapid response team (RRT) to deal with it.[7, 8, 9, 10] We previously demonstrated that a relatively simple hospital‐specific method for generating EWS alerts derived from the electronic medical record (EMR) database is capable of predicting clinical deterioration and the need for ICU transfer, as well as hospital mortality, in non‐ICU patients admitted to general inpatient medicine units.[11, 12, 13, 14] However, our data also showed that simply providing the EWS alerts to these nursing units did not result in any demonstrable improvement in patient outcomes.[14] Therefore, we set out to determine whether linking real‐time EWS alerts to an intervention and notification of the RRT for patient evaluation could improve the outcomes of patients cared for on general inpatient units.
METHODS
Study Location
The study was conducted on 8 adult inpatient medicine units of Barnes‐Jewish Hospital, a 1250‐bed academic medical center in St. Louis, MO (January 15, 2013May 9, 2013). Patient care on the inpatient medicine units is delivered by either attending hospitalist physicians or dedicated housestaff physicians under the supervision of an attending physician. Continuous electronic vital sign monitoring is not provided on these units. The study was approved by the Washington University School of Medicine Human Studies Committee, and informed consent was waived. This was a nonblinded study (
Patients and Procedures
Patients admitted to the 8 medicine units received usual care during the study except as noted below. Manually obtained vital signs, laboratory data, and pharmacy data inputted in real time into the EMR were continuously assessed. The EWS searched for the 36 input variables previously described[11, 14] from the EMR for all patients admitted to the 8 medicine units 24 hours per day and 7 days a week. Values for every continuous parameter were scaled so that all measurements lay in the interval (0, 1) and were normalized by the minimum and maximum of the parameter as previously described.[14] To capture the temporal effects in our data, we retained a sliding window of all the collected data points within the last 24 hours. We then subdivided these data into a series of 6 sequential buckets of 4 hours each. We excluded the 2 hours of data prior to ICU transfer in building the model (so the data were 26 hours to 2 hours prior to ICU transfer for ICU transfer patients, and the first 24 hours of admission for everyone else). Eligible patients were selected for study entry when they triggered an alert for clinical deterioration as determined by the EWS.[11, 14]
The EWS alert was implemented in an internally developed, Java‐based clinical decision support rules engine, which identified when new data relevant to the model were available in a real‐time central data repository. In a clinical application, it is important to capture unusual changes in vital‐sign data over time. Such changes may precede clinical deterioration by hours, providing a chance to intervene if detected early enough. In addition, not all readings in time‐series data should be treated equally; the value of some kinds of data may change depending on their age. For example, a patient's condition may be better reflected by a blood‐oxygenation reading collected 1 hour ago than a reading collected 12 hours ago. This is the rationale for our use of a sliding window of all collected data points within the last 24 hours performed on a real‐time basis to determine the alert status of the patient.[11, 14]
We applied various threshold cut points to convert the EWS alert predictions into binary values and compared the results against the actual ICU transfer outcome.[14] A threshold of 0.9760 for specificity was chosen to achieve a sensitivity of approximately 40%. These operating characteristics were chosen in turn to generate a manageable number of alerts per hospital nursing unit per day (estimated at 12 per nursing unit per day). At this cut point, the C statistic was 0.8834, with an overall accuracy of 0.9292. In other words, our EWS alert system is calibrated so that for every 1000 patient discharges per year from these 8 hospital units, there would be 75 patients generating an alert, of which 30 patients would be expected to have the study outcome (ie, clinical deterioration requiring ICU transfer).
Once patients on the study units were identified as at risk for clinical deterioration by the EWS, they were assigned by a computerized random number generator to the intervention group or the control group. The control group was managed according to the usual care provided on the medicine units. The EWS alerts generated for the control patients were electronically stored, but these alerts were not sent to the RRT nurse, instead they were hidden from all clinical staff. The intervention group had their EWS alerts sent real time to the nursing member of the hospital's RRT. The RRT is composed of a registered nurse, a second‐ or third‐year internal medicine resident, and a respiratory therapist. The RRT was introduced in 2009 for the study units involved in this investigation. For 2009, 2010, and 2011 the RRT nurse was pulled from the staff of 1 of the hospital's ICUs in a rotating manner to respond to calls to the RRT as they occurred. Starting in 2012, the RRT nurse was established as a dedicated position without other clinical responsibilities. The RRT nurse carries a hospital‐issued mobile phone, to which the automated alert messages were sent real time, and was instructed to respond to all EWS alerts within 20 minutes of their receipt.
The RRT nurse would initially evaluate the alerted intervention patients using the Modified Early Warning Score[15, 16] and make further clinical and triage decisions based on those criteria and discussions with the RRT physician or the patient's treating physicians. The RRT focused their interventions using an internally developed tool called the Four Ds (discuss goals of care, drugs needing to be administered, diagnostics needing to be performed, and damage control with the use of oxygen, intravenous fluids, ventilation, and blood products). Patients evaluated by the RRT could have their current level of care maintained, have the frequency of vital sign monitoring increased, be transferred to an ICU, or have a code blue called for emergent resuscitation. The RRT reviewed goals of care for all patients to determine the appropriateness of interventions, especially for patients near the end of life who did not desire intensive care interventions. Nursing staff on the hospital units could also make calls to the RRT for patient evaluation at any time based on their clinical assessments performed during routine nursing rounds.
The primary efficacy outcome was the need for ICU transfer. Secondary outcome measures were hospital mortality and hospital length of stay. Pertinent demographic, laboratory, and clinical data were gathered prospectively including age, gender, race, underlying comorbidities, and severity of illness assessed by the Charlson comorbidity score and Elixhauser comorbidities.[17, 18]
Statistical Analysis
We required a sample size of 514 patients (257 per group) to achieve 80% power at a 5% significance level, based on the superiority design, a baseline event rate for ICU transfer of 20.0%, and an absolute reduction of 8.0% (PS Power and Sample Size Calculations, version 3.0, Vanderbilt Biostatistics, Nashville, TN). Continuous variables were reported as means with standard deviations or medians with 25th and 75th percentiles according to their distribution. The Student t test was used when comparing normally distributed data, and the Mann‐Whitney U test was employed to analyze non‐normally distributed data (eg, hospital length of stay). Categorical data were expressed as frequency distributions, and the [2] test was used to determine if differences existed between groups. A P value <0.05 was regarded as statistically significant. An interim analysis was planned for the data safety monitoring board to evaluate patient safety after 50% of the patients were recruited. The primary analysis was by intention to treat. Analyses were performed using SPSS version 11.0 for Windows (SPSS, Inc., Chicago, IL).
Data Safety Monitoring Board
An independent data safety and monitoring board was convened to monitor the study and to review and approve protocol amendments by the steering committee.
RESULTS
Between January 15, 2013 and May 9, 2013, there were 4731 consecutive patients admitted to the 8 inpatient units and electronically screened as the base population for this investigation. Five hundred seventy‐one (12.1%) patients triggered an alert and were enrolled into the study (Figure 1). There were 286 patients assigned to the intervention group and 285 assigned to the control group. No patients were lost to follow‐up. Demographics, reason for hospital admission, and comorbidities of the 2 groups were similar (Table 1). The number of patients having a separate RRT call by the primary nursing team on the hospital units within 24 hours of generating an alert was greater for the intervention group but did not reach statistical significance (19.9% vs 16.5%; odds ratio: 1.260; 95% confidence interval [CI]: 0.8231.931). Table 2 provides the new diagnostic and therapeutic interventions initiated within 24 hours after a EWS alert was generated. Patients in the intervention group were significantly more likely to have their primary care team physician notified by an RRT nurse regarding medical condition issues and to have oximetry and telemetry started, whereas control patients were significantly more likely to have new antibiotic orders written within 24 hours of generating an alert.
| Variable | Intervention Group, n=286 | Control Group, n=285 | P Value |
|---|---|---|---|
| Age, y | 63.7 16.0 | 63.1 15.4 | 0.495 |
| Gender, n (%) | |||
| Male | 132 (46.2) | 140 (49.1) | 0.503 |
| Female | 154 (53.8) | 145 (50.9) | |
| Race, n (%) | |||
| Caucasian | 155 (54.2) | 154 (54.0) | 0.417 |
| African American | 105 (36.7) | 113 (39.6) | |
| Other | 26 (9.1) | 18 (6.3) | |
| Reason for hospital admission | |||
| Cardiac | 12 (4.2) | 15 (5.3) | 0.548 |
| Pulmonary | 64 (22.4) | 72 (25.3) | 0.418 |
| Underlying malignancy | 6 (2.1) | 3 (1.1) | 0.504 |
| Renal disease | 31 (10.8) | 22 (7.7) | 0.248 |
| Thromboembolism | 4 (1.4) | 5 (1.8) | 0.752 |
| Infection | 55 (19.2) | 50 (17.5) | 0.603 |
| Neurologic disease | 33 (11.5) | 22 (7.7) | 0.122 |
| Intra‐abdominal disease | 41 (14.3) | 47 (16.5) | 0.476 |
| Hematologic condition | 4 (1.4) | 5 (1.8) | 0.752 |
| Endocrine disorder | 12 (4.2) | 6 (2.1) | 0.153 |
| Source of hospital admission | |||
| Emergency department | 201 (70.3) | 203 (71.2) | 0.200 |
| Direct admission | 36 (12.6) | 46 (16.1) | |
| Hospital transfer | 49 (17.1) | 36 (12.6) | |
| Charlson score | 6.7 3.6 | 6.6 3.2 | 0.879 |
| Elixhauser comorbidities score | 7.4 3.5 | 7.5 3.4 | 0.839 |
| Variable | Intervention Group, n=286 | Control Group, n=285 | P Value |
|---|---|---|---|
| |||
| Medications, n (%) | |||
| Antibiotics | 92 (32.2) | 121 (42.5) | 0.011 |
| Antiarrhythmics | 48 (16.8) | 44 (15.4) | 0.662 |
| Anticoagulants | 83 (29.0) | 97 (34.0) | 0.197 |
| Diuretics/antihypertensives | 71 (24.8) | 55 (19.3) | 0.111 |
| Bronchodilators | 78 (27.3) | 73 (25.6) | 0.653 |
| Anticonvulsives | 26 (9.1) | 27 (9.5) | 0.875 |
| Sedatives/narcotics | 0 (0.0) | 1 (0.4) | 0.499 |
| Respiratory support, n (%) | |||
| Noninvasive ventilation | 17 (6.0) | 9 (3.1) | 0.106 |
| Escalated oxygen support | 12 (4.2) | 7 (2.5) | 0.247 |
| Enhanced vital signs, n (%) | 50 (17.5) | 47 (16.5) | 0.752 |
| Maintenance intravenous fluids, n (%) | 48 (16.8) | 41 (14.4) | 0.430 |
| Vasopressors, n (%) | 57 (19.9) | 61 (21.4) | 0.664 |
| Bolus intravenous fluids, n (%) | 7 (2.4) | 14 (4.9) | 0.118 |
| Telemetry, n (%) | 198 (69.2) | 176 (61.8) | 0.052 |
| Oximetry, n (%) | 20 (7.0) | 6 (2.1) | 0.005 |
| New intravenous access, n (%) | 26 (9.1) | 35 (12.3) | 0.217 |
| Primary care team physician called by RRT nurse, n (%) | 82 (28.7) | 56 (19.6) | 0.012 |
Fifty‐one patients (17.8%) randomly assigned to the intervention group required ICU transfer compared with 52 of 285 patients (18.2%) in the control group (odds ratio: 0.972; 95% CI: 0.6351.490; P=0.898) (Table 3). Twenty‐one patients (7.3%) randomly assigned to the intervention group expired during their hospitalization compared with 22 of 285 patients (7.7%) in the control group (odds ratio: 0.947; 95%CI: 0.5091.764; P=0.865). Hospital length of stay was 8.49.5 days (median, 4.5 days; interquartile range, 2.311.4 days) for patients randomized to the intervention group and 9.411.1 days (median, 5.3 days; interquartile range, 3.211.2 days) for patients randomized to the control group (P=0.038). The ICU length of stay was 4.86.6 days (median, 2.9 days; interquartile range, 1.76.5 days) for patients randomized to the intervention group and 5.86.4 days (median, 2.9 days; interquartile range, 1.57.4) for patients randomized to the control group (P=0.812).The number of patients requiring transfer to a nursing home or long‐term acute care hospital was similar for patients in the intervention and control groups (26.9% vs 26.3%; odds ratio: 1.032; 95% CI: 0.7121.495; P=0.870). Similarly, the number of patients requiring hospital readmission before 30 days and 180 days, respectively, was similar for the 2 treatment groups (Table 3). For the combined study population, the EWS alerts were triggered 94138 hours (median, 27 hours; interquartile range, 7132 hours) prior to ICU transfer and 250204 hours (median200 hours; interquartile range, 54347 hours) prior to hospital mortality. The number of RRT calls for the 8 medicine units studied progressively increased from the start of the RRT program in 2009 through 2013 (121 in 2009, 194 in 2010, 298 in 2011, 415 in 2012, 415 in 2013; P<0.001 for the trend).
| Outcome | Intervention Group, n=286 | Control Group, n=285 | P Value |
|---|---|---|---|
| |||
| ICU transfer, n (%) | 51 (17.8) | 52 (18.2) | 0.898 |
| All‐cause hospital mortality, n (%) | 21 (7.3) | 22 (7.7) | 0.865 |
| Transfer to nursing home or LTAC, n (%) | 77 (26.9) | 75 (26.3) | 0.870 |
| 30‐day readmission, n (%) | 53 (18.5) | 62 (21.8) | 0.337 |
| 180‐day readmission, n (%) | 124 (43.4) | 117 (41.1) | 0.577 |
| Hospital length of stay, d* | 8.49.5, 4.5 [2.311.4] | 9.411.1, 5.3 [3.211.2] | 0.038 |
| ICU length of stay, d* | 4.86.6, 2.9 [1.76.5] | 5.86.4, 2.9 [1.57.4] | 0.812 |
DISCUSSION
We demonstrated that a real‐time EWS alert sent to a RRT nurse was associated with a modest reduction in hospital length of stay, but similar rates of hospital mortality, ICU transfer, and subsequent need for placement in a long‐term care setting compared with usual care. We also found the number of RRT calls to have increased progressively from 2009 to the present on the study units examined.
Unplanned ICU transfers occurring as early as within 8 hours of hospitalization are relatively common and associated with increased mortality.[6] Bapoje et al. evaluated a total of 152 patients over 1 year who had unplanned ICU transfers.[19] The most common reason was worsening of the problem for which the patient was admitted (48%). Other investigators have also attempted to identify predictors for clinical deterioration resulting in unplanned ICU transfer that could be employed in an EWS.[20, 21] Organizations like the Institute for Healthcare Improvement have called for the development and routine implementation of EWSs to direct the activities of RRTs and improve outcomes.[22] However, a recent systematic review found that much of the evidence in support of EWSs and emergency response teams is of poor quality and lacking prospective randomized trials.[23]
Our earlier experience demonstrated that simply providing an alert to nursing units did not result in any demonstrable improvements in the outcomes of high‐risk patients identified by our EWS.[14] Previous investigations have also had difficulty in demonstrating consistent outcome improvements with the use of EWSs and RRTs.[24, 25, 26, 27, 28, 29, 30, 31, 32] As a result of mandates from quality improvement organizations, most US hospitals currently employ RRTs for emergent mobilization of resources when a clinically deteriorating patient is identified on a hospital ward.[33, 34] Linking RRT actions with a validated real‐time alert may represent a way of improving the overall effectiveness of such teams for monitoring general hospital units, short of having all hospitalized patients in units staffed and monitored to provide higher levels of supervision (eg, ICUs, step‐down units).[9, 35]
An alternative approach to preventing patient deterioration is to provide closer overall monitoring. This has been accomplished by employing nursing personnel to increase monitoring, or with the use of automated monitoring equipment. Bellomo et al. showed that the deployment of electronic automated vital sign monitors on general hospital units was associated with improved utilization of RRTs, increased patient survival, and decreased time for vital sign measurement and recording.[36] Laurens and Dwyer found that implementation of medical emergency teams (METs) to respond to predefined MET activation criteria as observed by hospital staff resulted in reduced hospital mortality and reduced need for ICU transfer.[37] However, other investigators have observed that imperfect implementation of nursing‐performed observational monitoring resulted in no demonstrable benefit, illustrating the limitations of this approach.[38] Our findings suggest that nursing care of patients on general hospital units may be enhanced with the use of an EWS alert sent to the RRT. This is supported by the observation that communications between the RRT and the primary care teams was greater as was the use of telemetry and oximetry in the intervention arm. Moreover, there appears to have been a learning effect for the nursing staff that occurred on our study units, as evidenced by the increased number of RRT calls that occurred between 2009 and 2013. This change in nursing practices on these units certainly made it more difficult for us to observe outcome differences in our current study with the prescribed intervention, reinforcing the notion that evaluating an already established practice is a difficult proposition.[39]
Our study has several important limitations. First, the EWS alert was developed and validated at Barnes‐Jewish Hospital.[11, 12, 13, 14] We cannot say whether this alert will perform similarly in another hospital. Second, the EWS alert only contains data from medical patients. Development and validation of EWS alerts for other hospitalized populations, including surgical and pediatric patients, are needed to make such systems more generalizable. Third, the primary clinical outcome employed for this trial was problematic. Transfer to an ICU may not be an optimal outcome variable, as it may be desirable to transfer alerted patients to an ICU, which can be perceived to represent a soft landing for such patients once an alert has been generated. A better measure could be 30‐day all‐cause mortality, which would not be subject to clinician biases. Finally, we could not specifically identify explanations for the greater use of antibiotics in the control group despite similar rates of infection for both study arms. Future studies should closely evaluate the ability of EWS alerts to alter specific therapies (eg, reduce antibiotic utilization).
In summary, we have demonstrated that an EWS alert linked to a RRT likely contributed to a modest reduction in hospital length of stay, but no reductions in hospital mortality and ICU transfer. These findings suggest that inpatient deterioration on general hospital units can be identified and linked to a specific intervention. Continued efforts are needed to identify and implement systems that will not only accurately identify high‐risk patients on general hospital units but also intervene to improve their outcomes. We are moving forward with the development of a 2‐tiered EWS utilizing both EMR data and real‐time streamed vital sign data, to determine if we can further improve the prediction of clinical deterioration and potentially intervene in a more clinically meaningful manner.
Acknowledgements
The authors thank Ann Doyle, BSN, Lisa Mayfield, BSN, and Darain Mitchell for their assistance in carrying out this research protocol; and William Shannon, PhD, from the Division of General Medical Sciences at Washington University, for statistical support.
Disclosures: This study was funded in part by the Barnes‐Jewish Hospital Foundation, the Chest Foundation of the American College of Chest Physicians, and by grant number UL1 RR024992 from the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH), and NIH Roadmap for Medical Research. Its contents are solely the responsibility of the authors and do not necessarily represent the official view of the NCRR or NIH. The steering committee was responsible for the study design, execution, analysis, and content of the article. The Barnes‐Jewish Hospital Foundation, the American College of Chest Physicians, and the Chest Foundation were not involved in the design, conduct, or analysis of the trial. The authors report no conflicts of interest. Marin Kollef, Yixin Chen, Kevin Heard, Gina LaRossa, Chenyang Lu, Nathan Martin, Nelda Martin, Scott Micek, and Thomas Bailey have all made substantial contributions to conception and design, or acquisition of data, or analysis and interpretation of data; have drafted the submitted article or revised it critically for important intellectual content; have provided final approval of the version to be published; and have agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
- , , , et al. Duration of life‐threatening antecedents prior to intensive care admission. Intensive Care Med. 2002;28(11):1629–1634.
- , , , et al. A comparison of antecedents to cardiac arrests, deaths and emergency intensive care admissions in Australia and New Zealand, and the United Kingdom—the ACADEMIA study. Resuscitation. 2004;62(3):275–282.
- , , . Abnormal vital signs are associated with an increased risk for critical events in US veteran inpatients. Resuscitation. 2009;80(11):1264–1269.
- , , , et al. Septic shock: an analysis of outcomes for patients with onset on hospital wards versus intensive care units. Crit Care Med. 1998;26(6):1020–1024.
- , , , , . Inpatient transfers to the intensive care unit: delays are associated with increased mortality and morbidity. J Gen Intern Med. 2003;18(2):77–83.
- , , , . Adverse outcomes associated with delayed intensive care unit transfers in an integrated healthcare system. J Hosp Med. 2012;7(3):224–230.
- , , , et al. Findings of the first consensus conference on medical emergency teams. Crit Care Med. 2006;34(9):2463–2478.
- , , , et al. “Identifying the hospitalised patient in crisis”—a consensus conference on the afferent limb of rapid response systems. Resuscitation. 2010;81(4):375–382.
- , , . Rapid‐response teams. N Engl J Med. 2011;365(2):139–146.
- , , , , . Acute care teams: a systematic review and meta‐analysis. Arch Intern Med. 2010;170(1):18–26.
- , , , et al. Toward a two‐tier clinical warning system for hospitalized patients. AMIA Annu Symp Proc. 2011;2011:511–519.
- , , , , , . Early prediction of septic shock in hospitalized patients. J Hosp Med. 2010;5(1):19–25.
- , , , et al. Implementation of a real‐time computerized sepsis alert in nonintensive care unit patients. Crit Care Med. 2011;39(3):469–473.
- , , , et al. A trial of a real‐time Alert for clinical deterioration in Patients hospitalized on general medical wards. J Hosp Med. 2013;8(5):236–242.
- , , , , . Prospective evaluation of a modified Early Warning Score to aid earlier detection of patients developing critical illness on a general surgical ward. Br J Anaesth. 2000;84:663P.
- , , , . Validation of a modified Early Warning Score in medical admissions. QJM. 2001;94(10):521–526.
- , , . Adapting a clinical comorbidity index for use with ICD‐9‐CM administrative databases. J Clin Epidemiol. 1992;45(6):613–619.
- , , , . Comorbidity measures for use with administrative data. Med Care. 1998;36(1):8–27.
- , , , . Unplanned transfers to a medical intensive care unit: causes and relationship to preventable errors in care. J Hosp Med. 2011;6(2):68–72.
- , , , , , . Unplanned transfers to the intensive care unit: the role of the shock index. J Hosp Med. 2010;5(8):460–465.
- , , , , , . Early detection of impending physiologic deterioration among patients who are not in intensive care: development of predictive models using data from an automated electronic medical record. J Hosp Med. 2012;7(5):388–395.
- Institute for Healthcare Improvement. Early warning systems: the next level of rapid response; 2011. Available at: http://www.ihi.org/Engage/Memberships/MentorHospitalRegistry/Pages/RapidResponseSystems.aspx. Accessed April 6, 2011.
- , . Do either early warning systems or emergency response teams improve hospital patient survival? A systematic review. Resuscitation. 2013;84(12):1652–1667.
- , , , et al. Introducing critical care outreach: a ward‐randomised trial of phased introduction in a general hospital. Intensive Care Med. 2004;30(7):1398–1404.
- . Out of our reach? Assessing the impact of introducing critical care outreach service. Anaesthesiology. 2003;58(9):882–885.
- , , . Effect of the critical care outreach team on patient survival to discharge from hospital and readmission to critical care: non‐randomised population based study. BMJ. 2003;327(7422):1014.
- , , . Reducing mortality and avoiding preventable ICU utilization: analysis of a successful rapid response program using APR DRGs. J Healthc Qual. 2011;33(5):7–16.
- , , , et al. Introduction of the medical emergency team (MET) system: a cluster‐randomised control trial. Lancet. 2005;365(9477):2091–2097.
- , , , , , . The impact of the introduction of critical care outreach services in England: a multicentre interrupted time‐series analysis. Crit Care. 2007;11(5):R113.
- , , , et al. Systematic review and evaluation of physiological track and trigger warning systems for identifying at‐risk patients on the ward. Intensive Care Med. 2007;33(4):667–679.
- , , , . Timing and teamwork—an observational pilot study of patients referred to a Rapid Response Team with the aim of identifying factors amenable to re‐design of a Rapid Response System. Resuscitation. 2012;83(6):782–787.
- , , , , , . The impact of rapid response team on outcome of patients transferred from the ward to the ICU: a single‐center study. Crit Care Med. 2013;41(10):2284–2291.
- , , , . Rapid response: a quality improvement conundrum. J Hosp Med. 2009;4(4):255–257.
- . Rapid response systems now established at 2,900 hospitals. Hospitalist News. 2010;3:1.
- , , . Early warning systems. Hosp Chron. 2012;7:37–43.
- , , , et al. A controlled trial of electronic automated advisory vital signs monitoring in general hospital wards. Crit Care Med. 2012;40(8):2349–2361.
- , . The impact of medical emergency teams on ICU admission rates, cardiopulmonary arrests and mortality in a regional hospital. Resuscitation. 2011;82(6):707–712.
- , , . Imperfect implementation of an early warning scoring system in a danish teaching hospital: a cross‐sectional study. PLoS One. 2013;8:e70068.
- , . Introduction of medical emergency teams in Australia and New Zealand: A multicentre study. Crit Care. 2008;12(3):151.
- , , , et al. Duration of life‐threatening antecedents prior to intensive care admission. Intensive Care Med. 2002;28(11):1629–1634.
- , , , et al. A comparison of antecedents to cardiac arrests, deaths and emergency intensive care admissions in Australia and New Zealand, and the United Kingdom—the ACADEMIA study. Resuscitation. 2004;62(3):275–282.
- , , . Abnormal vital signs are associated with an increased risk for critical events in US veteran inpatients. Resuscitation. 2009;80(11):1264–1269.
- , , , et al. Septic shock: an analysis of outcomes for patients with onset on hospital wards versus intensive care units. Crit Care Med. 1998;26(6):1020–1024.
- , , , , . Inpatient transfers to the intensive care unit: delays are associated with increased mortality and morbidity. J Gen Intern Med. 2003;18(2):77–83.
- , , , . Adverse outcomes associated with delayed intensive care unit transfers in an integrated healthcare system. J Hosp Med. 2012;7(3):224–230.
- , , , et al. Findings of the first consensus conference on medical emergency teams. Crit Care Med. 2006;34(9):2463–2478.
- , , , et al. “Identifying the hospitalised patient in crisis”—a consensus conference on the afferent limb of rapid response systems. Resuscitation. 2010;81(4):375–382.
- , , . Rapid‐response teams. N Engl J Med. 2011;365(2):139–146.
- , , , , . Acute care teams: a systematic review and meta‐analysis. Arch Intern Med. 2010;170(1):18–26.
- , , , et al. Toward a two‐tier clinical warning system for hospitalized patients. AMIA Annu Symp Proc. 2011;2011:511–519.
- , , , , , . Early prediction of septic shock in hospitalized patients. J Hosp Med. 2010;5(1):19–25.
- , , , et al. Implementation of a real‐time computerized sepsis alert in nonintensive care unit patients. Crit Care Med. 2011;39(3):469–473.
- , , , et al. A trial of a real‐time Alert for clinical deterioration in Patients hospitalized on general medical wards. J Hosp Med. 2013;8(5):236–242.
- , , , , . Prospective evaluation of a modified Early Warning Score to aid earlier detection of patients developing critical illness on a general surgical ward. Br J Anaesth. 2000;84:663P.
- , , , . Validation of a modified Early Warning Score in medical admissions. QJM. 2001;94(10):521–526.
- , , . Adapting a clinical comorbidity index for use with ICD‐9‐CM administrative databases. J Clin Epidemiol. 1992;45(6):613–619.
- , , , . Comorbidity measures for use with administrative data. Med Care. 1998;36(1):8–27.
- , , , . Unplanned transfers to a medical intensive care unit: causes and relationship to preventable errors in care. J Hosp Med. 2011;6(2):68–72.
- , , , , , . Unplanned transfers to the intensive care unit: the role of the shock index. J Hosp Med. 2010;5(8):460–465.
- , , , , , . Early detection of impending physiologic deterioration among patients who are not in intensive care: development of predictive models using data from an automated electronic medical record. J Hosp Med. 2012;7(5):388–395.
- Institute for Healthcare Improvement. Early warning systems: the next level of rapid response; 2011. Available at: http://www.ihi.org/Engage/Memberships/MentorHospitalRegistry/Pages/RapidResponseSystems.aspx. Accessed April 6, 2011.
- , . Do either early warning systems or emergency response teams improve hospital patient survival? A systematic review. Resuscitation. 2013;84(12):1652–1667.
- , , , et al. Introducing critical care outreach: a ward‐randomised trial of phased introduction in a general hospital. Intensive Care Med. 2004;30(7):1398–1404.
- . Out of our reach? Assessing the impact of introducing critical care outreach service. Anaesthesiology. 2003;58(9):882–885.
- , , . Effect of the critical care outreach team on patient survival to discharge from hospital and readmission to critical care: non‐randomised population based study. BMJ. 2003;327(7422):1014.
- , , . Reducing mortality and avoiding preventable ICU utilization: analysis of a successful rapid response program using APR DRGs. J Healthc Qual. 2011;33(5):7–16.
- , , , et al. Introduction of the medical emergency team (MET) system: a cluster‐randomised control trial. Lancet. 2005;365(9477):2091–2097.
- , , , , , . The impact of the introduction of critical care outreach services in England: a multicentre interrupted time‐series analysis. Crit Care. 2007;11(5):R113.
- , , , et al. Systematic review and evaluation of physiological track and trigger warning systems for identifying at‐risk patients on the ward. Intensive Care Med. 2007;33(4):667–679.
- , , , . Timing and teamwork—an observational pilot study of patients referred to a Rapid Response Team with the aim of identifying factors amenable to re‐design of a Rapid Response System. Resuscitation. 2012;83(6):782–787.
- , , , , , . The impact of rapid response team on outcome of patients transferred from the ward to the ICU: a single‐center study. Crit Care Med. 2013;41(10):2284–2291.
- , , , . Rapid response: a quality improvement conundrum. J Hosp Med. 2009;4(4):255–257.
- . Rapid response systems now established at 2,900 hospitals. Hospitalist News. 2010;3:1.
- , , . Early warning systems. Hosp Chron. 2012;7:37–43.
- , , , et al. A controlled trial of electronic automated advisory vital signs monitoring in general hospital wards. Crit Care Med. 2012;40(8):2349–2361.
- , . The impact of medical emergency teams on ICU admission rates, cardiopulmonary arrests and mortality in a regional hospital. Resuscitation. 2011;82(6):707–712.
- , , . Imperfect implementation of an early warning scoring system in a danish teaching hospital: a cross‐sectional study. PLoS One. 2013;8:e70068.
- , . Introduction of medical emergency teams in Australia and New Zealand: A multicentre study. Crit Care. 2008;12(3):151.
© 2014 Society of Hospital Medicine
Pharmacogenomic studies follow 90/10 rule
Credit: NIGMS
Few pharmacogenomic studies focus on orphan or tropical diseases prevalent in developing countries, according to research published in Global Public Health.
Researchers found that, from 1997 to 2010, pharmacogenomics research most commonly focused on cancers, depression or psychological disorders, and cardiovascular disease.
Less than 4% of publications dealt with orphan or infectious diseases.
According to the researchers, this suggests pharmacogenomic research follows the 90/10 rule.
“It is recognized that the distribution of technology and research follows the so-called 90/10 ratio rule; that is, 90% of global funding for health research, including the development drugs, is invested to treat 10% of the world’s population,” said study author Catherine Olivier, a PhD candidate at the University of Montreal’s School of Public Health.
This inequality between rich and poor countries led the United Nations (UN) to make the fight against HIV-AIDS, malaria, and neglected tropical diseases one of its 8 Millennium Development Goals, adopted in September 2000 by the 189 UN member states.
To verify the extent to which pharmacogenomic research has addressed orphan and tropical diseases, Olivier searched for pharmacogenomic studies published from 1997 to 2010. She identified 626 studies in 171 journals.
Each study was analyzed according to the type of disease it concerned, the origin of its authors, and their affiliation with pharmaceutical companies, if any.
“The information collected allowed us to draw a map showing current and historical trends in the development of pharmacogenomic research,” Olivier said.
She found that, from 1997 to 2003, there were 401 publications on pharmacogenomics in the PubMed database. And the majority of them (67%) were published in a single journal, Pharmacogenetics. Then, from 2003 to 2010, the number of studies doubled.
However, the apparent enthusiasm for this type of research seems to have been artificially inflated. Olivier noted that the percentage of nonoriginal publications, including reviews, meta-analyses, and debates, increased from 15% in 1997 to 51% in 2010.
“The number of original articles—that is, studies focusing on a new aspect of pharmacogenomics—began to decline after 2002,” Olivier said.
Moreover, during the period analyzed, nearly 23% of published studies in pharmacogenomics dealt with the area of oncology, followed by depression and psychological disorders (14.7%), and cardiovascular disorders (13.6%).
“Rare diseases, tropical infections, and maternal health, which should have benefited from pharmacogenomic research under the Millennium Development Goals, represented only 3.8% of published studies,” Olivier explained.
She noted that investigators from countries most likely to be interested in these areas of research conducted few studies on rare diseases and tropical infections.
“Of the 65 publications from BRICS countries—Brazil, Russia, India, China, and South Africa—only 2 concerned rare diseases and tropical infections,” Olivier said.
Yet these diseases represented nearly half (45.5%) of the main causes of mortality in underdeveloped countries, and 15% in developing countries, according to 2008 data issued by the UN.
“Unfortunately, our study indicates that we are far from fulfilling the promise to reduce health inequalities in the world,” Olivier said, “a promise which was made before the adoption of the Millennium Declaration.”
Credit: NIGMS
Few pharmacogenomic studies focus on orphan or tropical diseases prevalent in developing countries, according to research published in Global Public Health.
Researchers found that, from 1997 to 2010, pharmacogenomics research most commonly focused on cancers, depression or psychological disorders, and cardiovascular disease.
Less than 4% of publications dealt with orphan or infectious diseases.
According to the researchers, this suggests pharmacogenomic research follows the 90/10 rule.
“It is recognized that the distribution of technology and research follows the so-called 90/10 ratio rule; that is, 90% of global funding for health research, including the development drugs, is invested to treat 10% of the world’s population,” said study author Catherine Olivier, a PhD candidate at the University of Montreal’s School of Public Health.
This inequality between rich and poor countries led the United Nations (UN) to make the fight against HIV-AIDS, malaria, and neglected tropical diseases one of its 8 Millennium Development Goals, adopted in September 2000 by the 189 UN member states.
To verify the extent to which pharmacogenomic research has addressed orphan and tropical diseases, Olivier searched for pharmacogenomic studies published from 1997 to 2010. She identified 626 studies in 171 journals.
Each study was analyzed according to the type of disease it concerned, the origin of its authors, and their affiliation with pharmaceutical companies, if any.
“The information collected allowed us to draw a map showing current and historical trends in the development of pharmacogenomic research,” Olivier said.
She found that, from 1997 to 2003, there were 401 publications on pharmacogenomics in the PubMed database. And the majority of them (67%) were published in a single journal, Pharmacogenetics. Then, from 2003 to 2010, the number of studies doubled.
However, the apparent enthusiasm for this type of research seems to have been artificially inflated. Olivier noted that the percentage of nonoriginal publications, including reviews, meta-analyses, and debates, increased from 15% in 1997 to 51% in 2010.
“The number of original articles—that is, studies focusing on a new aspect of pharmacogenomics—began to decline after 2002,” Olivier said.
Moreover, during the period analyzed, nearly 23% of published studies in pharmacogenomics dealt with the area of oncology, followed by depression and psychological disorders (14.7%), and cardiovascular disorders (13.6%).
“Rare diseases, tropical infections, and maternal health, which should have benefited from pharmacogenomic research under the Millennium Development Goals, represented only 3.8% of published studies,” Olivier explained.
She noted that investigators from countries most likely to be interested in these areas of research conducted few studies on rare diseases and tropical infections.
“Of the 65 publications from BRICS countries—Brazil, Russia, India, China, and South Africa—only 2 concerned rare diseases and tropical infections,” Olivier said.
Yet these diseases represented nearly half (45.5%) of the main causes of mortality in underdeveloped countries, and 15% in developing countries, according to 2008 data issued by the UN.
“Unfortunately, our study indicates that we are far from fulfilling the promise to reduce health inequalities in the world,” Olivier said, “a promise which was made before the adoption of the Millennium Declaration.”
Credit: NIGMS
Few pharmacogenomic studies focus on orphan or tropical diseases prevalent in developing countries, according to research published in Global Public Health.
Researchers found that, from 1997 to 2010, pharmacogenomics research most commonly focused on cancers, depression or psychological disorders, and cardiovascular disease.
Less than 4% of publications dealt with orphan or infectious diseases.
According to the researchers, this suggests pharmacogenomic research follows the 90/10 rule.
“It is recognized that the distribution of technology and research follows the so-called 90/10 ratio rule; that is, 90% of global funding for health research, including the development drugs, is invested to treat 10% of the world’s population,” said study author Catherine Olivier, a PhD candidate at the University of Montreal’s School of Public Health.
This inequality between rich and poor countries led the United Nations (UN) to make the fight against HIV-AIDS, malaria, and neglected tropical diseases one of its 8 Millennium Development Goals, adopted in September 2000 by the 189 UN member states.
To verify the extent to which pharmacogenomic research has addressed orphan and tropical diseases, Olivier searched for pharmacogenomic studies published from 1997 to 2010. She identified 626 studies in 171 journals.
Each study was analyzed according to the type of disease it concerned, the origin of its authors, and their affiliation with pharmaceutical companies, if any.
“The information collected allowed us to draw a map showing current and historical trends in the development of pharmacogenomic research,” Olivier said.
She found that, from 1997 to 2003, there were 401 publications on pharmacogenomics in the PubMed database. And the majority of them (67%) were published in a single journal, Pharmacogenetics. Then, from 2003 to 2010, the number of studies doubled.
However, the apparent enthusiasm for this type of research seems to have been artificially inflated. Olivier noted that the percentage of nonoriginal publications, including reviews, meta-analyses, and debates, increased from 15% in 1997 to 51% in 2010.
“The number of original articles—that is, studies focusing on a new aspect of pharmacogenomics—began to decline after 2002,” Olivier said.
Moreover, during the period analyzed, nearly 23% of published studies in pharmacogenomics dealt with the area of oncology, followed by depression and psychological disorders (14.7%), and cardiovascular disorders (13.6%).
“Rare diseases, tropical infections, and maternal health, which should have benefited from pharmacogenomic research under the Millennium Development Goals, represented only 3.8% of published studies,” Olivier explained.
She noted that investigators from countries most likely to be interested in these areas of research conducted few studies on rare diseases and tropical infections.
“Of the 65 publications from BRICS countries—Brazil, Russia, India, China, and South Africa—only 2 concerned rare diseases and tropical infections,” Olivier said.
Yet these diseases represented nearly half (45.5%) of the main causes of mortality in underdeveloped countries, and 15% in developing countries, according to 2008 data issued by the UN.
“Unfortunately, our study indicates that we are far from fulfilling the promise to reduce health inequalities in the world,” Olivier said, “a promise which was made before the adoption of the Millennium Declaration.”
Nanocapsules prevent release of nontargeted radiation
Credit: Rhoda Baer
A novel type of nanocapsule can safely and effectively store isotopes that emit ionizing radiation, according to a paper published in Biochimica et Biophysica Acta.
In experiments, the nanocapsules were taken up by cells, accumulated near the perinuclear region, and persisted without degrading.
They also prevented nontargeted, radioactive daughter ions from escaping. These ions could cause significant damage if released, such as prompting the development of leukemia.
This research suggests the nanocapsules have the potential to advance radiation therapy, according to study author John M. Tomich, PhD, of Kansas State University’s Johnson Cancer Research Center.
Dr Tomich and his colleagues created the nanocapsules, called branched amphiphilic peptide capsules (BAPCs), by combining 2 related sequences of amino acids—bis(FLIVI)-K-KKKK and bis(FLIVIGSII)-K-KKKK.
“We found that the 2 sequences come together to form a thin membrane that assembled into little spheres, which we call capsules,” Dr Tomich said. “While other vesicles have been created from lipids, most are much less stable and break down. Ours are like stones, though. They’re incredibly stable and are not destroyed by cells in the body.”
The capsules’ ability to stay intact with the isotope inside and remain undetected by the body’s clearance systems prompted Dr Tomich to investigate using BAPCs for radiation therapies.
“The problem with current alpha-particle radiation therapies used to treat cancer is that they lead to the release of nontargeted, radioactive daughter ions into the body,” Dr Tomich said. “Radioactive atoms break down to form new atoms, called daughter ions, with the release of some form of energy or energetic particles. Alpha emitters give off an energetic particle that comes off at nearly the speed of light.”
The alpha particle destroys DNA and whatever vital cellular components are in its path. Similarly, the daughter ions recoil with high energy on ejection of the alpha particle. The daughter ions have enough energy to escape the targeting and containment molecules that are currently in use.
“Once freed, the daughter isotopes can end up in places you don’t want them, like bone marrow, which can then lead to leukemia and new challenges,” Dr Tomich said.
To see if the BAPCs could prevent the release of daughter isotopes, the researchers loaded the nanoparticles with 225Actinium. Upon decay, this compound releases 4 alpha particles and numerous daughter ions.
The team found that BAPCs loaded with the compound readily entered cells and migrated to a position alongside the nucleus.
As the alpha-particle-emitting isotopes decayed, the recoiled daughter ions collided with the capsule walls, essentially bouncing off them, and remained trapped inside the BAPCs. This completely blocked the release of the daughter ions, which prevented uptake in nontarget tissues.
Dr Tomich said more studies are needed to add target molecules to the surface of the BAPCs. But he believes the particles could provide a safer option for treating tumors with radiation therapy by reducing the amount of radioisotope required for killing cancer cells and reducing the side effects caused by off-target accumulation of radioisotopes.
“These capsules are easy to make and easy to work with,” Dr Tomich said. “I think we’re just scratching the surface of what we can do with them to improve human health and nanomaterials.”
Credit: Rhoda Baer
A novel type of nanocapsule can safely and effectively store isotopes that emit ionizing radiation, according to a paper published in Biochimica et Biophysica Acta.
In experiments, the nanocapsules were taken up by cells, accumulated near the perinuclear region, and persisted without degrading.
They also prevented nontargeted, radioactive daughter ions from escaping. These ions could cause significant damage if released, such as prompting the development of leukemia.
This research suggests the nanocapsules have the potential to advance radiation therapy, according to study author John M. Tomich, PhD, of Kansas State University’s Johnson Cancer Research Center.
Dr Tomich and his colleagues created the nanocapsules, called branched amphiphilic peptide capsules (BAPCs), by combining 2 related sequences of amino acids—bis(FLIVI)-K-KKKK and bis(FLIVIGSII)-K-KKKK.
“We found that the 2 sequences come together to form a thin membrane that assembled into little spheres, which we call capsules,” Dr Tomich said. “While other vesicles have been created from lipids, most are much less stable and break down. Ours are like stones, though. They’re incredibly stable and are not destroyed by cells in the body.”
The capsules’ ability to stay intact with the isotope inside and remain undetected by the body’s clearance systems prompted Dr Tomich to investigate using BAPCs for radiation therapies.
“The problem with current alpha-particle radiation therapies used to treat cancer is that they lead to the release of nontargeted, radioactive daughter ions into the body,” Dr Tomich said. “Radioactive atoms break down to form new atoms, called daughter ions, with the release of some form of energy or energetic particles. Alpha emitters give off an energetic particle that comes off at nearly the speed of light.”
The alpha particle destroys DNA and whatever vital cellular components are in its path. Similarly, the daughter ions recoil with high energy on ejection of the alpha particle. The daughter ions have enough energy to escape the targeting and containment molecules that are currently in use.
“Once freed, the daughter isotopes can end up in places you don’t want them, like bone marrow, which can then lead to leukemia and new challenges,” Dr Tomich said.
To see if the BAPCs could prevent the release of daughter isotopes, the researchers loaded the nanoparticles with 225Actinium. Upon decay, this compound releases 4 alpha particles and numerous daughter ions.
The team found that BAPCs loaded with the compound readily entered cells and migrated to a position alongside the nucleus.
As the alpha-particle-emitting isotopes decayed, the recoiled daughter ions collided with the capsule walls, essentially bouncing off them, and remained trapped inside the BAPCs. This completely blocked the release of the daughter ions, which prevented uptake in nontarget tissues.
Dr Tomich said more studies are needed to add target molecules to the surface of the BAPCs. But he believes the particles could provide a safer option for treating tumors with radiation therapy by reducing the amount of radioisotope required for killing cancer cells and reducing the side effects caused by off-target accumulation of radioisotopes.
“These capsules are easy to make and easy to work with,” Dr Tomich said. “I think we’re just scratching the surface of what we can do with them to improve human health and nanomaterials.”
Credit: Rhoda Baer
A novel type of nanocapsule can safely and effectively store isotopes that emit ionizing radiation, according to a paper published in Biochimica et Biophysica Acta.
In experiments, the nanocapsules were taken up by cells, accumulated near the perinuclear region, and persisted without degrading.
They also prevented nontargeted, radioactive daughter ions from escaping. These ions could cause significant damage if released, such as prompting the development of leukemia.
This research suggests the nanocapsules have the potential to advance radiation therapy, according to study author John M. Tomich, PhD, of Kansas State University’s Johnson Cancer Research Center.
Dr Tomich and his colleagues created the nanocapsules, called branched amphiphilic peptide capsules (BAPCs), by combining 2 related sequences of amino acids—bis(FLIVI)-K-KKKK and bis(FLIVIGSII)-K-KKKK.
“We found that the 2 sequences come together to form a thin membrane that assembled into little spheres, which we call capsules,” Dr Tomich said. “While other vesicles have been created from lipids, most are much less stable and break down. Ours are like stones, though. They’re incredibly stable and are not destroyed by cells in the body.”
The capsules’ ability to stay intact with the isotope inside and remain undetected by the body’s clearance systems prompted Dr Tomich to investigate using BAPCs for radiation therapies.
“The problem with current alpha-particle radiation therapies used to treat cancer is that they lead to the release of nontargeted, radioactive daughter ions into the body,” Dr Tomich said. “Radioactive atoms break down to form new atoms, called daughter ions, with the release of some form of energy or energetic particles. Alpha emitters give off an energetic particle that comes off at nearly the speed of light.”
The alpha particle destroys DNA and whatever vital cellular components are in its path. Similarly, the daughter ions recoil with high energy on ejection of the alpha particle. The daughter ions have enough energy to escape the targeting and containment molecules that are currently in use.
“Once freed, the daughter isotopes can end up in places you don’t want them, like bone marrow, which can then lead to leukemia and new challenges,” Dr Tomich said.
To see if the BAPCs could prevent the release of daughter isotopes, the researchers loaded the nanoparticles with 225Actinium. Upon decay, this compound releases 4 alpha particles and numerous daughter ions.
The team found that BAPCs loaded with the compound readily entered cells and migrated to a position alongside the nucleus.
As the alpha-particle-emitting isotopes decayed, the recoiled daughter ions collided with the capsule walls, essentially bouncing off them, and remained trapped inside the BAPCs. This completely blocked the release of the daughter ions, which prevented uptake in nontarget tissues.
Dr Tomich said more studies are needed to add target molecules to the surface of the BAPCs. But he believes the particles could provide a safer option for treating tumors with radiation therapy by reducing the amount of radioisotope required for killing cancer cells and reducing the side effects caused by off-target accumulation of radioisotopes.
“These capsules are easy to make and easy to work with,” Dr Tomich said. “I think we’re just scratching the surface of what we can do with them to improve human health and nanomaterials.”
Matching Workforce to Workload
Healthcare systems face many clinical and operational challenges in optimizing the quality of patient care across the domains of safety, effectiveness, efficiency, timeliness, patient‐centeredness, and equity.[1] They must also balance staff satisfaction, and in academic settings, the education of trainees. In inpatient settings, the process of care encompasses many microsystems, and clinical outcomes are the result of a combination of endogenous patient factors, the capabilities of clinical staff, as well as the static and dynamic organizational characteristics of the systems delivering care.[2, 3, 4, 5] Static organizational characteristics include hospital type and size, whereas dynamic organizational characteristics include communications between staff, staff fatigue, interruptions in care, and other factors that impact patient care and clinical outcomes (Figure 1).[2] Two major components of healthcare microsystems are workload and workforce.
A principle in operations management describes the need to match capacity (eg, workforce) to demand (eg, workload) to optimize efficiency.[6] This is particularly relevant in healthcare settings, where an excess of workload for the available workforce may negatively impact processes and outcomes of patient care and resident learning. These problems can arise from fatigue and strain from a heavy cognitive load, or from interruptions, distractions, and ineffective communication.[7, 8, 9, 10, 11] Conversely, in addition to being inefficient, an excess of workforce is financially disadvantageous for the hospital and reduces trainees' opportunities for learning.
Workload represents patient demand for clinical resources, including staff time and effort.[5, 12] Its elements include volume, turnover, acuity, and patient variety. Patient volume is measured by census.[12] Turnover refers to the number of admissions, discharges, and transfers in a given time period.[12] Acuity reflects the intensity of patient needs,[12] and variety represents the heterogeneity of those needs. These 4 workload factors are highly variable across locations and highly dynamic, even within a fixed location. Thus, measuring workload to assemble the appropriate workforce is challenging.
Workforce is comprised of clinical and nonclinical staff members who directly or indirectly provide services to patients. In this article, clinicians who obtain histories, conduct physical exams, write admission and progress notes, enter orders, communicate with consultants, and obtain consents are referred to as front‐line ordering clinicians (FLOCs). FLOCs perform activities listed in Table 1. Historically, in teaching hospitals, FLOCs consisted primarily of residents. More recently, FLOCs include nurse practitioners, physician assistants, house physicians, and hospitalists (when providing direct care and not supervising trainees).[13] In academic settings, supervising physicians (eg, senior supervising residents, fellows, or attendings), who are usually on the floor only in a supervisory capacity, may also contribute to FLOC tasks for part of their work time.
| FLOC Responsibilities | FLOC Personnel |
|---|---|
| |
| Admission history and physical exam | Residents |
| Daily interval histories | Nurse practitioners |
| Daily physical exams | Physician assistants |
| Obtaining consents | House physicians |
| Counseling, guidance, and case management | Hospitalists (when not in supervisory role) |
| Performing minor procedures | Fellows (when not in supervisory role) |
| Ordering, performing and interpreting diagnostic tests | Attendings (when not in supervisory role) |
| Writing prescriptions | |
Though matching workforce to workload is essential for hospital efficiency, staff satisfaction, and optimizing patient outcomes, hospitals currently lack a means to measure and match dynamic workload and workforce factors. This is particularly problematic at large children's hospitals, where high volumes of admitted patients stay for short amounts of time (less than 2 or 3 days).[14] This frequent turnover contributes significantly to workload. We sought to address this issue as part of a larger effort to redefine the care model at our urban, tertiary care children's hospital. This article describes our work to develop and obtain consensus for use of a tool to dynamically match FLOC workforce to clinical workload in a variety of inpatient settings.
METHODS
We undertook an iterative, multidisciplinary approach to develop the Care Model Matrix tool (Figure 2). The process involved literature reviews,[2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14] discussions with clinical leadership, and repeated validation sessions. Our focus was at the level of the patient nursing units, which are the discrete areas in a hospital where patient care is delivered and physician teams are organized. We met with physicians and nurses from every clinical care area at least twice to reach consensus on how to define model inputs, decide how to quantify those inputs for specific microsystems, and to validate whether model outputs seemed consistent with clinicians' experiences on the floors. For example, if the model indicated that a floor was short 1 FLOC during the nighttime period, relevant staff confirmed that this was consistent with their experience.
Quantifying Workload
In quantifying FLOC workload, we focused on 3 elements: volume, turnover, and acuity.[12] Volume is equal to the patient census at a moment in time for a particular floor or unit. Census data were extracted from the hospital's admission‐discharge‐transfer (ADT) system (Epic, Madison, WI). Timestamps for arrival and departure are available for each unit. These data were used to calculate census estimates for intervals of time that corresponded to activities such as rounds, conferences, or sign‐outs, and known variations in patient flow. Intervals for weekdays were: 7 am to 12 pm, 12 pm to 5 pm, 5 pm to 11 pm, and 11 pm to 7 am. Intervals for weekends were: 7 am to 7 pm (daytime), and 7 pm to 7 am (nighttime). Census data for each of the 6 intervals were averaged over 1 year.
In addition to patient volume, discussions with FLOCs highlighted the need to account for inpatients having different levels of need at different points throughout the day. For example, patients require the most attention in the morning, when FLOCs need to coordinate interval histories, conduct exams, enter orders, call consults, and interpret data. In the afternoon and overnight, patients already in beds have relatively fewer needs, especially in nonintensive care unit (ICU) settings. To adjust census data to account for time of day, a time factor was added, with 1 representing the normalized full morning workload (Figure 2, line 5). Based on clinical consensus, this time factor decreased over the course of the day, more so for non‐ICU patients than for ICU patients. For example, a time factor of 0.5 for overnight meant that patients in beds on that unit generated half as much work overnight as those same patients would in the morning when the time factor was set to 1. Multiplication of number of patients and the time factor equals adjusted census workload, which reflects what it felt like for FLOCs to care for that number of patients at that time. Specifically, if there were 20 patients at midnight with a time factor of 0.5, the patients generated a workload equal to 20 0.5=10 workload units (WU), whereas in the morning the same actual number of patients would generate a workload of 20 1=20 WU.
The ADT system was also used to track information about turnover, including number of admissions, discharges, and transfers in or out of each unit during each interval. Each turnover added to the workload count to reflect the work involved in admitting, transferring, or discharging a patient (Figure 2, lines 79). For example, a high‐turnover floor might have 20 patients in beds, with 4 admissions and 4 discharges in a given time period. Based on clinical consensus, it was determined that the work involved in managing each turnover would count as an additional workload element, yielding an adjusted census workload+turnover score of (20 1)+4+4=28 WU. Although only 20 patients would be counted in a static census during this time, the adjusted workload score was 28 WU. Like the time factor, this adjustment helps provide a feels‐like barometer.
Finally, this workload score is multiplied by an acuity factor that considers the intensity of need for patients on a unit (Figure 2, line 11). We stratified acuity based on whether the patient was in a general inpatient unit, a specialty unit, or an ICU, and assigned acuity factors based on observations of differences in intensity between those units. The acuity factor was normalized to 1 for patients on a regular inpatient floor. Specialty care areas were 20% higher (1.2), and ICUs were 40% higher (1.4). These differentials were estimated based on clinician experience and knowledge of current FLOC‐to‐patient and nurse‐to‐patient ratios.
Quantifying Workforce
To quantify workforce, we assumed that each FLOC, regardless of type, would be responsible for the same number of workload units. Limited evidence and research exist regarding ideal workload‐to‐staff ratios for FLOCs. Published literature and hospital experience suggest that the appropriate volume per trainee for non‐ICU inpatient care in medicine and pediatrics is between 6 and 10 patients (not workload units) per trainee.[13, 15, 16, 17, 18] Based on these data, we chose 8 workload units as a reasonable workload allocation per FLOC. This ratio appears in the matrix as a modifiable variable (Figure 2, line 14). We then divided total FLOC workload (Figure 2, line 15) from our workload calculations by 8 to determine total FLOC need (Figure 2, line 16). Because some of the workload captured in total FLOC need would be executed by personnel who are typically classified as non‐FLOCs, such as attendings, fellows, and supervising residents, we quantified the contributions of each of these non‐FLOCs through discussion with clinical leaders from each floor. For example, if an attending physician wrote complete notes on weekends, he or she would be contributing to FLOC work for that location on those days. A 0.2 contribution under attendings would mean that an attending contributed an amount of work equivalent to 20% of a FLOC. We subtracted contributions of non‐FLOCs from the total FLOC need to determine final FLOC need (Figure 2, line 22). Last, we subtracted the actual number of FLOCs assigned to a unit for a specific time period from the final FLOC need to determine the unit‐level FLOC gap at that time (Figure 2, line 24).
RESULTS
The Care Model Matrix compares predicted workforce need and actual workforce assignments, while considering the contributions of non‐FLOCs to FLOC work in various inpatient care settings. Figure 3 shows graphical representations of FLOC staffing models. The green line shows the traditional approach, and the red line shows the dynamic approach using the Care Model Matrix. The dynamic approach better captures variations in workload.
We presented the tool at over 25 meetings in 14 hospital divisions, and received widespread acceptance among physician, nursing, and administrative leadership. In addition, the hospital has used the tool to identify gaps in FLOC coverage and guide hiring and staffing decisions. Each clinical area also used the tool to review staffing for the 2012 academic year. Though a formal evaluation of the tool has not been conducted, feedback from attending physicians and FLOCs has been positive. Specifically, staffing adjustments have increased the available workforce in the afternoons and on weekends, when floors were previously perceived to be understaffed.
DISCUSSION
Hospitals depend upon a large, diverse workforce to manage and care for patients. In any system there will be a threshold at which workload exceeds the available workforce. In healthcare delivery settings, this can harm patient care and resident education.[12, 19] Conversely, a workforce that is larger than necessary is inefficient. If hospitals can define and measure relevant elements to better match workforce to workload, they can avoid under or over supplying staff, and mitigate the risks associated with an overburdened workforce or the waste of unused capacity. It also enables more flexible care models to dynamically match resources to needs.
The Care Model Matrix is a flexible, objective tool that quantifies multidimensional aspects of workload and workforce. With the tool, hospitals can use historic data on census, turnover, and acuity to predict workload and staffing needs at specific time periods. Managers can also identify discrepancies between workload and workforce, and match them more efficiently during the day.
The tool, which uses multiple modifiable variables, can be adapted to a variety of academic and community inpatient settings. Although our sample numbers in Figure 2 represent census, turnover, acuity, and workload‐to‐FLOC ratios at our hospital, other hospitals can adjust the model to reflect their numbers. The flexibility to add new factors as elements of workload or workforce enhances usability. For example, the model can be modified to capture other factors that affect staffing needs such as frequency of handoffs[11] and the staff's level of education or experience.
There are, however, numerous challenges associated with matching FLOC staffing to workload. Although there is a 24‐hour demand for FLOC coverage, unlike nursing, ideal FLOC to patients or workload ratios have not been established. Academic hospitals may experience additional challenges, because trainees have academic responsibilities in addition to clinical roles. Although trainees are included in FLOC counts, they are unavailable during certain didactic times, and their absence may affect the workload balance.
Another challenge associated with dynamically adjusting workforce to workload is that most hospitals do not have extensive flex or surge capacity. One way to address this is to have FLOCs choose days when they will be available as backup for a floor that is experiencing a heavier than expected workload. Similarly, when floors are experiencing a lighter than expected workload, additional FLOCs can be diverted to administrative tasks, to other floors in need of extra capacity, or sent home with the expectation that the day will be made up when the floor is experiencing a heavier workload.
Though the tool provides numerous advantages, there are several limitations to consider. First, the time and acuity factors used in the workload calculation, as well as the non‐FLOC contribution estimates and numbers reflecting desired workload per FLOC used in the workforce calculation, are somewhat subjective estimations based on observation and staff consensus. Thus, even though the tool's approach should be generalizable to any hospital, the specific values may not be. Therefore, other hospitals may need to change these values based on their unique situations. It is also worth noting that the flexibility of the tool presents both a virtue and potential vice. Those using the tool must agree upon a standard to define units so inconsistent definitions do not introduce unjustified discrepancies in workload. Second, the current tool does not consider the costs and benefits of different staffing approaches. Different types of FLOCs may handle workload differently, so an ideal combination of FLOC types should be considered in future studies. Third, although this work focused on matching FLOCs to workload, the appropriate matching of other workforce members is also essential to maximizing efficiency and patient care. Finally, because the tool has not yet been tested against outcomes, adhering to the tool's suggested ratios cannot necessary guarantee optimal outcomes in terms of patient care or provider satisfaction. Rather, the tool is designed to detect mismatches of workload and workforce based on desired workload levels, defined through local consensus.
CONCLUSION
We sought to develop a tool that quantifies workload and workforce to help our freestanding children's hospital predict and plan for future staffing needs. We created a tool that is objective and flexible, and can be applied to a variety of academic and community inpatient settings to identify mismatches of workload and workforce at discrete time intervals. However, given that the tool's recommendations are sensitive to model inputs that are based on local consensus, further research is necessary to test the validity and generalizability of the tool in various settings. Model inputs may need to be calibrated over time to maximize the tool's usefulness in a particular setting. Further study is also needed to determine how the tool directly impacts patient and provider satisfaction and the quality of care delivered.
Acknowledgements
The authors acknowledge the dozens of physicians and nurses for their involvement in the development of the Care Model Matrix through repeated meetings and dialog. The authors thank Sheyla Medina, Lawrence Chang, and Jennifer Jonas for their assistance in the production of this article.
Disclosures: Internal funds from The Children's Hospital of Philadelphia supported the conduct of this work. The authors have no financial interests, relationships, affiliations, or potential conflicts of interest relevant to the subject matter or materials discussed in the manuscript to disclose.
- . A user's manual for the IOM's “quality chasm” report. Health Aff. 2002;21(3):80–90.
- . Human error: models and management. BMJ. 2000;320(7237):768–770.
- , . Knowledge for Improvement: Improving Quality in the Micro‐systems of Care. in Providing Quality of Care in a Cost‐Focused Environment, Goldfield N, Nach DB (eds.), Gaithersburg, Maryland: Aspen Publishers, Inc. 1999;75–88.
- World Alliance For Patient Safety Drafting Group1, , , , et al. Towards an International Classification for Patient Safety: the conceptual framework. Int J Qual Health Care. F2009;21(1):2–8.
- , . Impact of workload on service time and patient safety: an econometric analysis of hospital operations. Manage Sci. 2009;55(9):1486–1498.
- , . Matching Supply With Demand: An Introduction to Operations Management. New York, NY: McGraw‐Hill; 2006.
- , . Operational failures and interruptions in hospital nursing. Health Serv Res. 2006;41:643–662.
- , , , , . Association of interruptions with an increased risk and severity of medication administration errors. Arch Intern Med. 2010;170(8):683–690.
- . The impact of fatigue on patient safety. Pediatr Clin North Am. 2006;53(6):1135–1153.
- , , , , . Effects of hospital care environment on patient mortality and nurse outcomes. J Nurs Adm. 2009;39(7/8):S45–S51.
- , , , , , . Perspective: beyond counting hours: the importance of supervision, professionalism, transitions of care, and workload in residency training. Acad Med. 2012;87(7):883–888.
- , , , et al. Hospital workload and adverse events. Med Care. 2007;45(5):448–455.
- , . Resident Work Hours, Hospitalist Programs, and Academic Medical Centers. The Hospitalist. Vol Jan/Feb: Society of Hospital Medicine; 2005: http://www.the‐hospitalist.org/details/article/257983/Resident_Work_Hours_Hospitalist_Programs_and_Academic_Medical_Centers.html#. Accessed on August 21, 2012.
- . Hospital stays for children, 2006. Healthcare Cost and Utilization Project. Statistical brief 56. Rockville, MD: Agency for Healthcare Research and Quality; 2008. Available at: http://www.hcup‐us.ahrq.gov/reports/statbriefs/sb56.pdf. Accessed on August 21, 2012
- , , , et al. Implications of the California nurse staffing mandate for other states. Health Serv Res. 2010;45:904–921.
- . Patient safety at ten: unmistakable progress, troubling gaps. Health Aff. 2010;29(1):165–173.
- , , , et al. Patient‐to‐nurse ratios and outcomes of moderately preterm infants. Pediatrics. 2010;125(2):320–326.
- , , , , . Nurse‐staffing levels and the quality of care in hospitals. N Engl J Med. 2002;346(22):1715–1722.
- , , , . Perceptions of educational experience and inpatient workload among pediatric residents. Hosp Pediatr. 2013;3(3):276–284.
Healthcare systems face many clinical and operational challenges in optimizing the quality of patient care across the domains of safety, effectiveness, efficiency, timeliness, patient‐centeredness, and equity.[1] They must also balance staff satisfaction, and in academic settings, the education of trainees. In inpatient settings, the process of care encompasses many microsystems, and clinical outcomes are the result of a combination of endogenous patient factors, the capabilities of clinical staff, as well as the static and dynamic organizational characteristics of the systems delivering care.[2, 3, 4, 5] Static organizational characteristics include hospital type and size, whereas dynamic organizational characteristics include communications between staff, staff fatigue, interruptions in care, and other factors that impact patient care and clinical outcomes (Figure 1).[2] Two major components of healthcare microsystems are workload and workforce.
A principle in operations management describes the need to match capacity (eg, workforce) to demand (eg, workload) to optimize efficiency.[6] This is particularly relevant in healthcare settings, where an excess of workload for the available workforce may negatively impact processes and outcomes of patient care and resident learning. These problems can arise from fatigue and strain from a heavy cognitive load, or from interruptions, distractions, and ineffective communication.[7, 8, 9, 10, 11] Conversely, in addition to being inefficient, an excess of workforce is financially disadvantageous for the hospital and reduces trainees' opportunities for learning.
Workload represents patient demand for clinical resources, including staff time and effort.[5, 12] Its elements include volume, turnover, acuity, and patient variety. Patient volume is measured by census.[12] Turnover refers to the number of admissions, discharges, and transfers in a given time period.[12] Acuity reflects the intensity of patient needs,[12] and variety represents the heterogeneity of those needs. These 4 workload factors are highly variable across locations and highly dynamic, even within a fixed location. Thus, measuring workload to assemble the appropriate workforce is challenging.
Workforce is comprised of clinical and nonclinical staff members who directly or indirectly provide services to patients. In this article, clinicians who obtain histories, conduct physical exams, write admission and progress notes, enter orders, communicate with consultants, and obtain consents are referred to as front‐line ordering clinicians (FLOCs). FLOCs perform activities listed in Table 1. Historically, in teaching hospitals, FLOCs consisted primarily of residents. More recently, FLOCs include nurse practitioners, physician assistants, house physicians, and hospitalists (when providing direct care and not supervising trainees).[13] In academic settings, supervising physicians (eg, senior supervising residents, fellows, or attendings), who are usually on the floor only in a supervisory capacity, may also contribute to FLOC tasks for part of their work time.
| FLOC Responsibilities | FLOC Personnel |
|---|---|
| |
| Admission history and physical exam | Residents |
| Daily interval histories | Nurse practitioners |
| Daily physical exams | Physician assistants |
| Obtaining consents | House physicians |
| Counseling, guidance, and case management | Hospitalists (when not in supervisory role) |
| Performing minor procedures | Fellows (when not in supervisory role) |
| Ordering, performing and interpreting diagnostic tests | Attendings (when not in supervisory role) |
| Writing prescriptions | |
Though matching workforce to workload is essential for hospital efficiency, staff satisfaction, and optimizing patient outcomes, hospitals currently lack a means to measure and match dynamic workload and workforce factors. This is particularly problematic at large children's hospitals, where high volumes of admitted patients stay for short amounts of time (less than 2 or 3 days).[14] This frequent turnover contributes significantly to workload. We sought to address this issue as part of a larger effort to redefine the care model at our urban, tertiary care children's hospital. This article describes our work to develop and obtain consensus for use of a tool to dynamically match FLOC workforce to clinical workload in a variety of inpatient settings.
METHODS
We undertook an iterative, multidisciplinary approach to develop the Care Model Matrix tool (Figure 2). The process involved literature reviews,[2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14] discussions with clinical leadership, and repeated validation sessions. Our focus was at the level of the patient nursing units, which are the discrete areas in a hospital where patient care is delivered and physician teams are organized. We met with physicians and nurses from every clinical care area at least twice to reach consensus on how to define model inputs, decide how to quantify those inputs for specific microsystems, and to validate whether model outputs seemed consistent with clinicians' experiences on the floors. For example, if the model indicated that a floor was short 1 FLOC during the nighttime period, relevant staff confirmed that this was consistent with their experience.
Quantifying Workload
In quantifying FLOC workload, we focused on 3 elements: volume, turnover, and acuity.[12] Volume is equal to the patient census at a moment in time for a particular floor or unit. Census data were extracted from the hospital's admission‐discharge‐transfer (ADT) system (Epic, Madison, WI). Timestamps for arrival and departure are available for each unit. These data were used to calculate census estimates for intervals of time that corresponded to activities such as rounds, conferences, or sign‐outs, and known variations in patient flow. Intervals for weekdays were: 7 am to 12 pm, 12 pm to 5 pm, 5 pm to 11 pm, and 11 pm to 7 am. Intervals for weekends were: 7 am to 7 pm (daytime), and 7 pm to 7 am (nighttime). Census data for each of the 6 intervals were averaged over 1 year.
In addition to patient volume, discussions with FLOCs highlighted the need to account for inpatients having different levels of need at different points throughout the day. For example, patients require the most attention in the morning, when FLOCs need to coordinate interval histories, conduct exams, enter orders, call consults, and interpret data. In the afternoon and overnight, patients already in beds have relatively fewer needs, especially in nonintensive care unit (ICU) settings. To adjust census data to account for time of day, a time factor was added, with 1 representing the normalized full morning workload (Figure 2, line 5). Based on clinical consensus, this time factor decreased over the course of the day, more so for non‐ICU patients than for ICU patients. For example, a time factor of 0.5 for overnight meant that patients in beds on that unit generated half as much work overnight as those same patients would in the morning when the time factor was set to 1. Multiplication of number of patients and the time factor equals adjusted census workload, which reflects what it felt like for FLOCs to care for that number of patients at that time. Specifically, if there were 20 patients at midnight with a time factor of 0.5, the patients generated a workload equal to 20 0.5=10 workload units (WU), whereas in the morning the same actual number of patients would generate a workload of 20 1=20 WU.
The ADT system was also used to track information about turnover, including number of admissions, discharges, and transfers in or out of each unit during each interval. Each turnover added to the workload count to reflect the work involved in admitting, transferring, or discharging a patient (Figure 2, lines 79). For example, a high‐turnover floor might have 20 patients in beds, with 4 admissions and 4 discharges in a given time period. Based on clinical consensus, it was determined that the work involved in managing each turnover would count as an additional workload element, yielding an adjusted census workload+turnover score of (20 1)+4+4=28 WU. Although only 20 patients would be counted in a static census during this time, the adjusted workload score was 28 WU. Like the time factor, this adjustment helps provide a feels‐like barometer.
Finally, this workload score is multiplied by an acuity factor that considers the intensity of need for patients on a unit (Figure 2, line 11). We stratified acuity based on whether the patient was in a general inpatient unit, a specialty unit, or an ICU, and assigned acuity factors based on observations of differences in intensity between those units. The acuity factor was normalized to 1 for patients on a regular inpatient floor. Specialty care areas were 20% higher (1.2), and ICUs were 40% higher (1.4). These differentials were estimated based on clinician experience and knowledge of current FLOC‐to‐patient and nurse‐to‐patient ratios.
Quantifying Workforce
To quantify workforce, we assumed that each FLOC, regardless of type, would be responsible for the same number of workload units. Limited evidence and research exist regarding ideal workload‐to‐staff ratios for FLOCs. Published literature and hospital experience suggest that the appropriate volume per trainee for non‐ICU inpatient care in medicine and pediatrics is between 6 and 10 patients (not workload units) per trainee.[13, 15, 16, 17, 18] Based on these data, we chose 8 workload units as a reasonable workload allocation per FLOC. This ratio appears in the matrix as a modifiable variable (Figure 2, line 14). We then divided total FLOC workload (Figure 2, line 15) from our workload calculations by 8 to determine total FLOC need (Figure 2, line 16). Because some of the workload captured in total FLOC need would be executed by personnel who are typically classified as non‐FLOCs, such as attendings, fellows, and supervising residents, we quantified the contributions of each of these non‐FLOCs through discussion with clinical leaders from each floor. For example, if an attending physician wrote complete notes on weekends, he or she would be contributing to FLOC work for that location on those days. A 0.2 contribution under attendings would mean that an attending contributed an amount of work equivalent to 20% of a FLOC. We subtracted contributions of non‐FLOCs from the total FLOC need to determine final FLOC need (Figure 2, line 22). Last, we subtracted the actual number of FLOCs assigned to a unit for a specific time period from the final FLOC need to determine the unit‐level FLOC gap at that time (Figure 2, line 24).
RESULTS
The Care Model Matrix compares predicted workforce need and actual workforce assignments, while considering the contributions of non‐FLOCs to FLOC work in various inpatient care settings. Figure 3 shows graphical representations of FLOC staffing models. The green line shows the traditional approach, and the red line shows the dynamic approach using the Care Model Matrix. The dynamic approach better captures variations in workload.
We presented the tool at over 25 meetings in 14 hospital divisions, and received widespread acceptance among physician, nursing, and administrative leadership. In addition, the hospital has used the tool to identify gaps in FLOC coverage and guide hiring and staffing decisions. Each clinical area also used the tool to review staffing for the 2012 academic year. Though a formal evaluation of the tool has not been conducted, feedback from attending physicians and FLOCs has been positive. Specifically, staffing adjustments have increased the available workforce in the afternoons and on weekends, when floors were previously perceived to be understaffed.
DISCUSSION
Hospitals depend upon a large, diverse workforce to manage and care for patients. In any system there will be a threshold at which workload exceeds the available workforce. In healthcare delivery settings, this can harm patient care and resident education.[12, 19] Conversely, a workforce that is larger than necessary is inefficient. If hospitals can define and measure relevant elements to better match workforce to workload, they can avoid under or over supplying staff, and mitigate the risks associated with an overburdened workforce or the waste of unused capacity. It also enables more flexible care models to dynamically match resources to needs.
The Care Model Matrix is a flexible, objective tool that quantifies multidimensional aspects of workload and workforce. With the tool, hospitals can use historic data on census, turnover, and acuity to predict workload and staffing needs at specific time periods. Managers can also identify discrepancies between workload and workforce, and match them more efficiently during the day.
The tool, which uses multiple modifiable variables, can be adapted to a variety of academic and community inpatient settings. Although our sample numbers in Figure 2 represent census, turnover, acuity, and workload‐to‐FLOC ratios at our hospital, other hospitals can adjust the model to reflect their numbers. The flexibility to add new factors as elements of workload or workforce enhances usability. For example, the model can be modified to capture other factors that affect staffing needs such as frequency of handoffs[11] and the staff's level of education or experience.
There are, however, numerous challenges associated with matching FLOC staffing to workload. Although there is a 24‐hour demand for FLOC coverage, unlike nursing, ideal FLOC to patients or workload ratios have not been established. Academic hospitals may experience additional challenges, because trainees have academic responsibilities in addition to clinical roles. Although trainees are included in FLOC counts, they are unavailable during certain didactic times, and their absence may affect the workload balance.
Another challenge associated with dynamically adjusting workforce to workload is that most hospitals do not have extensive flex or surge capacity. One way to address this is to have FLOCs choose days when they will be available as backup for a floor that is experiencing a heavier than expected workload. Similarly, when floors are experiencing a lighter than expected workload, additional FLOCs can be diverted to administrative tasks, to other floors in need of extra capacity, or sent home with the expectation that the day will be made up when the floor is experiencing a heavier workload.
Though the tool provides numerous advantages, there are several limitations to consider. First, the time and acuity factors used in the workload calculation, as well as the non‐FLOC contribution estimates and numbers reflecting desired workload per FLOC used in the workforce calculation, are somewhat subjective estimations based on observation and staff consensus. Thus, even though the tool's approach should be generalizable to any hospital, the specific values may not be. Therefore, other hospitals may need to change these values based on their unique situations. It is also worth noting that the flexibility of the tool presents both a virtue and potential vice. Those using the tool must agree upon a standard to define units so inconsistent definitions do not introduce unjustified discrepancies in workload. Second, the current tool does not consider the costs and benefits of different staffing approaches. Different types of FLOCs may handle workload differently, so an ideal combination of FLOC types should be considered in future studies. Third, although this work focused on matching FLOCs to workload, the appropriate matching of other workforce members is also essential to maximizing efficiency and patient care. Finally, because the tool has not yet been tested against outcomes, adhering to the tool's suggested ratios cannot necessary guarantee optimal outcomes in terms of patient care or provider satisfaction. Rather, the tool is designed to detect mismatches of workload and workforce based on desired workload levels, defined through local consensus.
CONCLUSION
We sought to develop a tool that quantifies workload and workforce to help our freestanding children's hospital predict and plan for future staffing needs. We created a tool that is objective and flexible, and can be applied to a variety of academic and community inpatient settings to identify mismatches of workload and workforce at discrete time intervals. However, given that the tool's recommendations are sensitive to model inputs that are based on local consensus, further research is necessary to test the validity and generalizability of the tool in various settings. Model inputs may need to be calibrated over time to maximize the tool's usefulness in a particular setting. Further study is also needed to determine how the tool directly impacts patient and provider satisfaction and the quality of care delivered.
Acknowledgements
The authors acknowledge the dozens of physicians and nurses for their involvement in the development of the Care Model Matrix through repeated meetings and dialog. The authors thank Sheyla Medina, Lawrence Chang, and Jennifer Jonas for their assistance in the production of this article.
Disclosures: Internal funds from The Children's Hospital of Philadelphia supported the conduct of this work. The authors have no financial interests, relationships, affiliations, or potential conflicts of interest relevant to the subject matter or materials discussed in the manuscript to disclose.
Healthcare systems face many clinical and operational challenges in optimizing the quality of patient care across the domains of safety, effectiveness, efficiency, timeliness, patient‐centeredness, and equity.[1] They must also balance staff satisfaction, and in academic settings, the education of trainees. In inpatient settings, the process of care encompasses many microsystems, and clinical outcomes are the result of a combination of endogenous patient factors, the capabilities of clinical staff, as well as the static and dynamic organizational characteristics of the systems delivering care.[2, 3, 4, 5] Static organizational characteristics include hospital type and size, whereas dynamic organizational characteristics include communications between staff, staff fatigue, interruptions in care, and other factors that impact patient care and clinical outcomes (Figure 1).[2] Two major components of healthcare microsystems are workload and workforce.
A principle in operations management describes the need to match capacity (eg, workforce) to demand (eg, workload) to optimize efficiency.[6] This is particularly relevant in healthcare settings, where an excess of workload for the available workforce may negatively impact processes and outcomes of patient care and resident learning. These problems can arise from fatigue and strain from a heavy cognitive load, or from interruptions, distractions, and ineffective communication.[7, 8, 9, 10, 11] Conversely, in addition to being inefficient, an excess of workforce is financially disadvantageous for the hospital and reduces trainees' opportunities for learning.
Workload represents patient demand for clinical resources, including staff time and effort.[5, 12] Its elements include volume, turnover, acuity, and patient variety. Patient volume is measured by census.[12] Turnover refers to the number of admissions, discharges, and transfers in a given time period.[12] Acuity reflects the intensity of patient needs,[12] and variety represents the heterogeneity of those needs. These 4 workload factors are highly variable across locations and highly dynamic, even within a fixed location. Thus, measuring workload to assemble the appropriate workforce is challenging.
Workforce is comprised of clinical and nonclinical staff members who directly or indirectly provide services to patients. In this article, clinicians who obtain histories, conduct physical exams, write admission and progress notes, enter orders, communicate with consultants, and obtain consents are referred to as front‐line ordering clinicians (FLOCs). FLOCs perform activities listed in Table 1. Historically, in teaching hospitals, FLOCs consisted primarily of residents. More recently, FLOCs include nurse practitioners, physician assistants, house physicians, and hospitalists (when providing direct care and not supervising trainees).[13] In academic settings, supervising physicians (eg, senior supervising residents, fellows, or attendings), who are usually on the floor only in a supervisory capacity, may also contribute to FLOC tasks for part of their work time.
| FLOC Responsibilities | FLOC Personnel |
|---|---|
| |
| Admission history and physical exam | Residents |
| Daily interval histories | Nurse practitioners |
| Daily physical exams | Physician assistants |
| Obtaining consents | House physicians |
| Counseling, guidance, and case management | Hospitalists (when not in supervisory role) |
| Performing minor procedures | Fellows (when not in supervisory role) |
| Ordering, performing and interpreting diagnostic tests | Attendings (when not in supervisory role) |
| Writing prescriptions | |
Though matching workforce to workload is essential for hospital efficiency, staff satisfaction, and optimizing patient outcomes, hospitals currently lack a means to measure and match dynamic workload and workforce factors. This is particularly problematic at large children's hospitals, where high volumes of admitted patients stay for short amounts of time (less than 2 or 3 days).[14] This frequent turnover contributes significantly to workload. We sought to address this issue as part of a larger effort to redefine the care model at our urban, tertiary care children's hospital. This article describes our work to develop and obtain consensus for use of a tool to dynamically match FLOC workforce to clinical workload in a variety of inpatient settings.
METHODS
We undertook an iterative, multidisciplinary approach to develop the Care Model Matrix tool (Figure 2). The process involved literature reviews,[2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14] discussions with clinical leadership, and repeated validation sessions. Our focus was at the level of the patient nursing units, which are the discrete areas in a hospital where patient care is delivered and physician teams are organized. We met with physicians and nurses from every clinical care area at least twice to reach consensus on how to define model inputs, decide how to quantify those inputs for specific microsystems, and to validate whether model outputs seemed consistent with clinicians' experiences on the floors. For example, if the model indicated that a floor was short 1 FLOC during the nighttime period, relevant staff confirmed that this was consistent with their experience.
Quantifying Workload
In quantifying FLOC workload, we focused on 3 elements: volume, turnover, and acuity.[12] Volume is equal to the patient census at a moment in time for a particular floor or unit. Census data were extracted from the hospital's admission‐discharge‐transfer (ADT) system (Epic, Madison, WI). Timestamps for arrival and departure are available for each unit. These data were used to calculate census estimates for intervals of time that corresponded to activities such as rounds, conferences, or sign‐outs, and known variations in patient flow. Intervals for weekdays were: 7 am to 12 pm, 12 pm to 5 pm, 5 pm to 11 pm, and 11 pm to 7 am. Intervals for weekends were: 7 am to 7 pm (daytime), and 7 pm to 7 am (nighttime). Census data for each of the 6 intervals were averaged over 1 year.
In addition to patient volume, discussions with FLOCs highlighted the need to account for inpatients having different levels of need at different points throughout the day. For example, patients require the most attention in the morning, when FLOCs need to coordinate interval histories, conduct exams, enter orders, call consults, and interpret data. In the afternoon and overnight, patients already in beds have relatively fewer needs, especially in nonintensive care unit (ICU) settings. To adjust census data to account for time of day, a time factor was added, with 1 representing the normalized full morning workload (Figure 2, line 5). Based on clinical consensus, this time factor decreased over the course of the day, more so for non‐ICU patients than for ICU patients. For example, a time factor of 0.5 for overnight meant that patients in beds on that unit generated half as much work overnight as those same patients would in the morning when the time factor was set to 1. Multiplication of number of patients and the time factor equals adjusted census workload, which reflects what it felt like for FLOCs to care for that number of patients at that time. Specifically, if there were 20 patients at midnight with a time factor of 0.5, the patients generated a workload equal to 20 0.5=10 workload units (WU), whereas in the morning the same actual number of patients would generate a workload of 20 1=20 WU.
The ADT system was also used to track information about turnover, including number of admissions, discharges, and transfers in or out of each unit during each interval. Each turnover added to the workload count to reflect the work involved in admitting, transferring, or discharging a patient (Figure 2, lines 79). For example, a high‐turnover floor might have 20 patients in beds, with 4 admissions and 4 discharges in a given time period. Based on clinical consensus, it was determined that the work involved in managing each turnover would count as an additional workload element, yielding an adjusted census workload+turnover score of (20 1)+4+4=28 WU. Although only 20 patients would be counted in a static census during this time, the adjusted workload score was 28 WU. Like the time factor, this adjustment helps provide a feels‐like barometer.
Finally, this workload score is multiplied by an acuity factor that considers the intensity of need for patients on a unit (Figure 2, line 11). We stratified acuity based on whether the patient was in a general inpatient unit, a specialty unit, or an ICU, and assigned acuity factors based on observations of differences in intensity between those units. The acuity factor was normalized to 1 for patients on a regular inpatient floor. Specialty care areas were 20% higher (1.2), and ICUs were 40% higher (1.4). These differentials were estimated based on clinician experience and knowledge of current FLOC‐to‐patient and nurse‐to‐patient ratios.
Quantifying Workforce
To quantify workforce, we assumed that each FLOC, regardless of type, would be responsible for the same number of workload units. Limited evidence and research exist regarding ideal workload‐to‐staff ratios for FLOCs. Published literature and hospital experience suggest that the appropriate volume per trainee for non‐ICU inpatient care in medicine and pediatrics is between 6 and 10 patients (not workload units) per trainee.[13, 15, 16, 17, 18] Based on these data, we chose 8 workload units as a reasonable workload allocation per FLOC. This ratio appears in the matrix as a modifiable variable (Figure 2, line 14). We then divided total FLOC workload (Figure 2, line 15) from our workload calculations by 8 to determine total FLOC need (Figure 2, line 16). Because some of the workload captured in total FLOC need would be executed by personnel who are typically classified as non‐FLOCs, such as attendings, fellows, and supervising residents, we quantified the contributions of each of these non‐FLOCs through discussion with clinical leaders from each floor. For example, if an attending physician wrote complete notes on weekends, he or she would be contributing to FLOC work for that location on those days. A 0.2 contribution under attendings would mean that an attending contributed an amount of work equivalent to 20% of a FLOC. We subtracted contributions of non‐FLOCs from the total FLOC need to determine final FLOC need (Figure 2, line 22). Last, we subtracted the actual number of FLOCs assigned to a unit for a specific time period from the final FLOC need to determine the unit‐level FLOC gap at that time (Figure 2, line 24).
RESULTS
The Care Model Matrix compares predicted workforce need and actual workforce assignments, while considering the contributions of non‐FLOCs to FLOC work in various inpatient care settings. Figure 3 shows graphical representations of FLOC staffing models. The green line shows the traditional approach, and the red line shows the dynamic approach using the Care Model Matrix. The dynamic approach better captures variations in workload.
We presented the tool at over 25 meetings in 14 hospital divisions, and received widespread acceptance among physician, nursing, and administrative leadership. In addition, the hospital has used the tool to identify gaps in FLOC coverage and guide hiring and staffing decisions. Each clinical area also used the tool to review staffing for the 2012 academic year. Though a formal evaluation of the tool has not been conducted, feedback from attending physicians and FLOCs has been positive. Specifically, staffing adjustments have increased the available workforce in the afternoons and on weekends, when floors were previously perceived to be understaffed.
DISCUSSION
Hospitals depend upon a large, diverse workforce to manage and care for patients. In any system there will be a threshold at which workload exceeds the available workforce. In healthcare delivery settings, this can harm patient care and resident education.[12, 19] Conversely, a workforce that is larger than necessary is inefficient. If hospitals can define and measure relevant elements to better match workforce to workload, they can avoid under or over supplying staff, and mitigate the risks associated with an overburdened workforce or the waste of unused capacity. It also enables more flexible care models to dynamically match resources to needs.
The Care Model Matrix is a flexible, objective tool that quantifies multidimensional aspects of workload and workforce. With the tool, hospitals can use historic data on census, turnover, and acuity to predict workload and staffing needs at specific time periods. Managers can also identify discrepancies between workload and workforce, and match them more efficiently during the day.
The tool, which uses multiple modifiable variables, can be adapted to a variety of academic and community inpatient settings. Although our sample numbers in Figure 2 represent census, turnover, acuity, and workload‐to‐FLOC ratios at our hospital, other hospitals can adjust the model to reflect their numbers. The flexibility to add new factors as elements of workload or workforce enhances usability. For example, the model can be modified to capture other factors that affect staffing needs such as frequency of handoffs[11] and the staff's level of education or experience.
There are, however, numerous challenges associated with matching FLOC staffing to workload. Although there is a 24‐hour demand for FLOC coverage, unlike nursing, ideal FLOC to patients or workload ratios have not been established. Academic hospitals may experience additional challenges, because trainees have academic responsibilities in addition to clinical roles. Although trainees are included in FLOC counts, they are unavailable during certain didactic times, and their absence may affect the workload balance.
Another challenge associated with dynamically adjusting workforce to workload is that most hospitals do not have extensive flex or surge capacity. One way to address this is to have FLOCs choose days when they will be available as backup for a floor that is experiencing a heavier than expected workload. Similarly, when floors are experiencing a lighter than expected workload, additional FLOCs can be diverted to administrative tasks, to other floors in need of extra capacity, or sent home with the expectation that the day will be made up when the floor is experiencing a heavier workload.
Though the tool provides numerous advantages, there are several limitations to consider. First, the time and acuity factors used in the workload calculation, as well as the non‐FLOC contribution estimates and numbers reflecting desired workload per FLOC used in the workforce calculation, are somewhat subjective estimations based on observation and staff consensus. Thus, even though the tool's approach should be generalizable to any hospital, the specific values may not be. Therefore, other hospitals may need to change these values based on their unique situations. It is also worth noting that the flexibility of the tool presents both a virtue and potential vice. Those using the tool must agree upon a standard to define units so inconsistent definitions do not introduce unjustified discrepancies in workload. Second, the current tool does not consider the costs and benefits of different staffing approaches. Different types of FLOCs may handle workload differently, so an ideal combination of FLOC types should be considered in future studies. Third, although this work focused on matching FLOCs to workload, the appropriate matching of other workforce members is also essential to maximizing efficiency and patient care. Finally, because the tool has not yet been tested against outcomes, adhering to the tool's suggested ratios cannot necessary guarantee optimal outcomes in terms of patient care or provider satisfaction. Rather, the tool is designed to detect mismatches of workload and workforce based on desired workload levels, defined through local consensus.
CONCLUSION
We sought to develop a tool that quantifies workload and workforce to help our freestanding children's hospital predict and plan for future staffing needs. We created a tool that is objective and flexible, and can be applied to a variety of academic and community inpatient settings to identify mismatches of workload and workforce at discrete time intervals. However, given that the tool's recommendations are sensitive to model inputs that are based on local consensus, further research is necessary to test the validity and generalizability of the tool in various settings. Model inputs may need to be calibrated over time to maximize the tool's usefulness in a particular setting. Further study is also needed to determine how the tool directly impacts patient and provider satisfaction and the quality of care delivered.
Acknowledgements
The authors acknowledge the dozens of physicians and nurses for their involvement in the development of the Care Model Matrix through repeated meetings and dialog. The authors thank Sheyla Medina, Lawrence Chang, and Jennifer Jonas for their assistance in the production of this article.
Disclosures: Internal funds from The Children's Hospital of Philadelphia supported the conduct of this work. The authors have no financial interests, relationships, affiliations, or potential conflicts of interest relevant to the subject matter or materials discussed in the manuscript to disclose.
- . A user's manual for the IOM's “quality chasm” report. Health Aff. 2002;21(3):80–90.
- . Human error: models and management. BMJ. 2000;320(7237):768–770.
- , . Knowledge for Improvement: Improving Quality in the Micro‐systems of Care. in Providing Quality of Care in a Cost‐Focused Environment, Goldfield N, Nach DB (eds.), Gaithersburg, Maryland: Aspen Publishers, Inc. 1999;75–88.
- World Alliance For Patient Safety Drafting Group1, , , , et al. Towards an International Classification for Patient Safety: the conceptual framework. Int J Qual Health Care. F2009;21(1):2–8.
- , . Impact of workload on service time and patient safety: an econometric analysis of hospital operations. Manage Sci. 2009;55(9):1486–1498.
- , . Matching Supply With Demand: An Introduction to Operations Management. New York, NY: McGraw‐Hill; 2006.
- , . Operational failures and interruptions in hospital nursing. Health Serv Res. 2006;41:643–662.
- , , , , . Association of interruptions with an increased risk and severity of medication administration errors. Arch Intern Med. 2010;170(8):683–690.
- . The impact of fatigue on patient safety. Pediatr Clin North Am. 2006;53(6):1135–1153.
- , , , , . Effects of hospital care environment on patient mortality and nurse outcomes. J Nurs Adm. 2009;39(7/8):S45–S51.
- , , , , , . Perspective: beyond counting hours: the importance of supervision, professionalism, transitions of care, and workload in residency training. Acad Med. 2012;87(7):883–888.
- , , , et al. Hospital workload and adverse events. Med Care. 2007;45(5):448–455.
- , . Resident Work Hours, Hospitalist Programs, and Academic Medical Centers. The Hospitalist. Vol Jan/Feb: Society of Hospital Medicine; 2005: http://www.the‐hospitalist.org/details/article/257983/Resident_Work_Hours_Hospitalist_Programs_and_Academic_Medical_Centers.html#. Accessed on August 21, 2012.
- . Hospital stays for children, 2006. Healthcare Cost and Utilization Project. Statistical brief 56. Rockville, MD: Agency for Healthcare Research and Quality; 2008. Available at: http://www.hcup‐us.ahrq.gov/reports/statbriefs/sb56.pdf. Accessed on August 21, 2012
- , , , et al. Implications of the California nurse staffing mandate for other states. Health Serv Res. 2010;45:904–921.
- . Patient safety at ten: unmistakable progress, troubling gaps. Health Aff. 2010;29(1):165–173.
- , , , et al. Patient‐to‐nurse ratios and outcomes of moderately preterm infants. Pediatrics. 2010;125(2):320–326.
- , , , , . Nurse‐staffing levels and the quality of care in hospitals. N Engl J Med. 2002;346(22):1715–1722.
- , , , . Perceptions of educational experience and inpatient workload among pediatric residents. Hosp Pediatr. 2013;3(3):276–284.
- . A user's manual for the IOM's “quality chasm” report. Health Aff. 2002;21(3):80–90.
- . Human error: models and management. BMJ. 2000;320(7237):768–770.
- , . Knowledge for Improvement: Improving Quality in the Micro‐systems of Care. in Providing Quality of Care in a Cost‐Focused Environment, Goldfield N, Nach DB (eds.), Gaithersburg, Maryland: Aspen Publishers, Inc. 1999;75–88.
- World Alliance For Patient Safety Drafting Group1, , , , et al. Towards an International Classification for Patient Safety: the conceptual framework. Int J Qual Health Care. F2009;21(1):2–8.
- , . Impact of workload on service time and patient safety: an econometric analysis of hospital operations. Manage Sci. 2009;55(9):1486–1498.
- , . Matching Supply With Demand: An Introduction to Operations Management. New York, NY: McGraw‐Hill; 2006.
- , . Operational failures and interruptions in hospital nursing. Health Serv Res. 2006;41:643–662.
- , , , , . Association of interruptions with an increased risk and severity of medication administration errors. Arch Intern Med. 2010;170(8):683–690.
- . The impact of fatigue on patient safety. Pediatr Clin North Am. 2006;53(6):1135–1153.
- , , , , . Effects of hospital care environment on patient mortality and nurse outcomes. J Nurs Adm. 2009;39(7/8):S45–S51.
- , , , , , . Perspective: beyond counting hours: the importance of supervision, professionalism, transitions of care, and workload in residency training. Acad Med. 2012;87(7):883–888.
- , , , et al. Hospital workload and adverse events. Med Care. 2007;45(5):448–455.
- , . Resident Work Hours, Hospitalist Programs, and Academic Medical Centers. The Hospitalist. Vol Jan/Feb: Society of Hospital Medicine; 2005: http://www.the‐hospitalist.org/details/article/257983/Resident_Work_Hours_Hospitalist_Programs_and_Academic_Medical_Centers.html#. Accessed on August 21, 2012.
- . Hospital stays for children, 2006. Healthcare Cost and Utilization Project. Statistical brief 56. Rockville, MD: Agency for Healthcare Research and Quality; 2008. Available at: http://www.hcup‐us.ahrq.gov/reports/statbriefs/sb56.pdf. Accessed on August 21, 2012
- , , , et al. Implications of the California nurse staffing mandate for other states. Health Serv Res. 2010;45:904–921.
- . Patient safety at ten: unmistakable progress, troubling gaps. Health Aff. 2010;29(1):165–173.
- , , , et al. Patient‐to‐nurse ratios and outcomes of moderately preterm infants. Pediatrics. 2010;125(2):320–326.
- , , , , . Nurse‐staffing levels and the quality of care in hospitals. N Engl J Med. 2002;346(22):1715–1722.
- , , , . Perceptions of educational experience and inpatient workload among pediatric residents. Hosp Pediatr. 2013;3(3):276–284.
