Preventable and mitigable adverse events in cancer care: Measuring risk and harm across the continuum
Abstract
BACKGROUND
Patient safety is a critical concern in clinical oncology, but the ability to measure adverse events (AEs) across cancer care is limited by a narrow focus on treatment-related toxicities. The objective of this study was to assess the nature and extent of AEs among cancer patients across inpatient and outpatient settings.
METHODS
This was a retrospective cohort study of 400 adult patients selected by stratified random sampling who had breast (n = 128), colorectal (n = 136), or lung cancer (n = 136) treated at a comprehensive cancer center in 2012. Candidate AEs, or injuries due to medical care, were identified by trained nurse reviewers over the course of 1 year from medical records and safety-reporting databases. Physicians determined the AE harm severity and the likelihood of preventability and harm mitigation.
RESULTS
The 400-patient sample represented 133,358 days of follow-up. Three hundred four AEs were identified for an overall rate of 2.3 events per 1000 patient days (91.2 per 1000 inpatient days and 0.9 per 1000 outpatient days). Thirty-four percent of the patients had 1 or more AEs (95% confidence interval, 29%-39%), and 16% of the patients had 1 or more preventable or mitigable AEs (95% confidence interval, 13%-20%). The AE rate for patients with breast cancer was lower than the rate for patients with colorectal or lung cancer (P ≤ .001). The preventable or mitigable AE rate was 0.9 per 1000 patient days. Six percent of AEs and 4% of preventable AEs resulted in serious harm. Examples included lymphedema, abscess, and renal failure.
CONCLUSIONS
A heavy burden of AEs, including preventable or mitigable events, has been identified. Future research should examine risk factors and improvement strategies for reducing their burden. Cancer 2017;123:4728-4736. © 2017 American Cancer Society.
INTRODUCTION
Patient safety is a key focus in clinical oncology. Successful therapy requires a thoughtful balance of treatment-related toxicities and long-term recurrence-free survival. The management of toxicities is a core competence of oncology practitioners, and advances in therapeutics have relied heavily on innovations in symptom management, dose titration, and supportive therapies.
With the intense focus on treatment-related toxicities, it is surprising that oncology lags other areas of medicine in its understanding of the nature and extent of medical errors and injuries. The ability to measure the frequency, spectrum, and preventability of adverse events (AEs) among cancer patients and across settings is limited to a few small and dated studies.1, 2 In light of the potential severity of illness among cancer patients, its myriad manifestations and complications, and the toxicities of therapy, this is an area of unmet need. A deeper understanding of the nature and extent of AEs in cancer care may offer insights that can inform interventions to improve patient outcomes and reduce suffering.
The few studies of patient safety in oncology suggest that AEs are common and harmful. In one study of patients receiving outpatient chemotherapy at a single cancer center in 2000, the medication order error rate was 3%, and 2% of the errors had the potential for harm.1 In a more recent study of 4 US outpatient oncology clinics, the rate of potentially injurious errors was 5 per 1000 medication orders in adults and 10 per 1000 in children.2 These rates of errors and AEs in ambulatory oncology are low in comparison with the rates of adverse drug events in studies of hospitalized (5%-10%)3-11 and ambulatory general medicine patients (25%),1, 12 and this raises the possibility of a systematic underestimation of the problem's magnitude.
Although clinicians can recognize the risk of harm for individual patients, empirical evidence is limited concerning the rate at which mistakes are made, the types of mistakes, their impact on patients, and opportunities for improvement. Therefore, we conducted a study to assess the nature and extent of harm in the form of AEs, or unintended harm associated with medical care, among cancer patients longitudinally across inpatient and outpatient care at a comprehensive cancer center. We focused on preventable events and events whose severity or duration of harm could have been mitigated.
MATERIALS AND METHODS
We conducted a retrospective cohort study of 400 patients at a single comprehensive cancer center to identify AEs (ie, injuries associated with medical care) and potentially preventable or mitigable AEs during the course of cancer care in both inpatient and outpatient settings.
Setting and Participants
We conducted this study at Memorial Sloan Kettering Cancer Center (MSK), a New York–based, National Cancer Institute–designated comprehensive cancer center with a full range of inpatient and outpatient services for adult and pediatric cancer patients. We studied a cohort of 400 adult patients who were 18 years old or older and were diagnosed with breast, colorectal, or lung cancer. Patients began their first cancer-directed treatment between January 1, 2012 and December 31, 2012. We followed each patient in our study cohort for up to 1 year or death, whichever came first.
Sampling Strategy
We planned to accrue a total of 400 patients with breast, colorectal, and lung cancers for the final sample. We selected an initial cohort of 600 potential subjects (200 subjects with each cancer type) to make sure that we had complete records for all patients in our final sample.
To ensure a diverse and balanced sample within each cancer type, we designed a stratified random sampling strategy by the stage of disease and the type of treatment received. We consulted clinicians with expertise in each cancer type to design the sampling strategy specific to that cancer type. For the breast cancer cohort, we randomly selected 75 patients with stage 0 to III disease with chemotherapy, 75 patients with stage 0 to III disease without chemotherapy, and 50 patients with stage IV disease. For the colorectal cancer cohort, we randomly selected 50 patients with stage I to III colon cancer, 50 patients with stage I to III rectal cancer, and 100 patients with stage IV colorectal cancer. For the lung cancer cohort, we randomly selected 90 patients with stage I to III non–small cell lung cancer, 10 patients with limited-stage small cell lung cancer, and 100 patients with extensive-stage small cell lung cancer/stage IV non–small cell lung cancer.
We ordered the 600 subjects for the medical record review to yield a balanced sample, regardless of the final sample size (see Table 1 for the final cohort characteristics by sampling strata). No records were missing for the patient sample that we selected. We completed data abstraction for 400 patients.
| Characteristic | Patients (n = 400) |
|---|---|
| Breast cancer cohort | |
| Total, No. | 128 |
| Stages 0-III with chemotherapy, % | 46 |
| Stages 0-III without chemotherapy, % | 28 |
| Stage IV, % | 26 |
| Colorectal cancer cohort | |
| Total, No. | 136 |
| Stage I-III colon cancer, % | 24 |
| Stage I-III rectal cancer, % | 24 |
| Stage IV colorectal cancer, % | 52 |
| Lung cancer cohort | |
| Total, No. | 136 |
| Stage I-III NSCLC, % | 44 |
| Limited-stage SCLC, % | 6 |
| Stage IV NSCLC/extensive-stage SCLC, % | 50 |
- Abbreviations: NSCLC, non–small cell lung cancer; SCLC, small cell lung cancer.
- To ensure a diverse and balanced sample, we used random stratified sampling to select the patient cohort.
Data Sources
MSK uses a homegrown health care information system (HIS) for the storage and retrieval of patient information across settings within the institution. The HIS is an integrated clinical application that includes laboratory, pathology, and radiology test results, surgical procedures, chemotherapy and pharmacy profiles, treatment pathways and guidelines, an order management system, electronic order entry and electronic signature capabilities, the electronic medical record, and patient characteristics. Inpatient and outpatients records are included in the HIS.
Information about AEs that are reported by clinicians is maintained at MSK in the Surgical Secondary Events and RL6:RISQ systems (RL Solutions, Toronto, Ontario, Canada). These systems are separate from the HIS. The Surgical Secondary Events database includes AE reports that are submitted by clinicians for surgical cases. RL6:RISQ includes reports of AEs, errors, and near misses that are submitted voluntarily by any front-line staff member.
Study Outcomes
AEs
An AE was defined as unintended harm to the patient by an act of commission or omission rather than the underlying disease or condition of the patient.6, 13, 14 Adverse drug events are included as a subset of AEs.
Preventable and mitigable AEs
An event was deemed preventable if the AE resulted from clinical care that was inconsistent with standard oncology practice or from a treatment-related complication that should have been anticipated. Events deemed not likely to be preventable were further evaluated for their likelihood of being mitigable. An event was deemed mitigable if the severity or duration of harm could have been lessened had clinicians acted promptly and appropriately.15 The likelihood of preventability and mitigation was classified as follows: definitely, probably, probably not, definitely not, or unable to determine.
Severity of harm
We defined the severity of harm associated with AEs according to the National Coordinating Council for Medication Error Reporting and Prevention index.16 We included categories D to I: (D) the event required monitoring to confirm that it resulted in no harm to the patient and/or required intervention to preclude harm, (E) the event contributed to or resulted in temporary harm to the patient and required intervention, (F) the event contributed to or resulted in temporary harm to the patient and required initial or prolonged hospitalization, (G) the event contributed to or resulted in permanent patient harm, (H) the event contributed to or required intervention to sustain life, and (I) the event contributed to the patient's death.
Patient Demographic and Administrative Variables
Patient demographic and administrative variables included the following: age, sex, race, ethnicity, non-English primary language, marital status, insurance status, comorbid conditions, disease stage, and cancer treatments received (ie, chemotherapy, radiation therapy, and surgery). These variables were obtained electronically from the HIS except for comorbid conditions, which were manually abstracted by physician reviewers from patient intake forms.
Data Abstraction
To identify candidate AEs, experienced oncology nurses reviewed all available medical records for each patient for 1 year after the first cancer-directed therapy at MSK. Records included information about inpatient and outpatient care at MSK, tests and treatments received at MSK, and any information about non-MSK care that was recorded or archived in the HIS.
To facilitate the nurses' record review, the study team developed an oncology-based trigger tool.17, 18 Trigger tools have been developed and deployed in various clinical settings to enhance the efficiency and accuracy of medical record reviews.19-22 In our previous research, we developed an oncology-specific trigger tool with good performance characteristics.17, 18 This tool included a list of 76 distinct triggers or signals that would lead reviewers to a focused review of the record for the occurrence of AEs. Examples of triggers included a “return to the operating room or interventional radiology within 30 days of surgery” and “elevated blood glucose (>250 mg/dL).” We obtained structured input from a multidisciplinary panel of clinician experts to select triggers for inclusion in the tool. The overall positive predictive value of the tool was 0.40 for total AEs and 0.15 for preventable or mitigable AEs, which are rates that exceed the performance of standard indicators for quality and patient safety.23
If a trigger was identified in the medical record, the nurse reviewer performed a more detailed record abstraction to assess whether a candidate AE occurred. For candidate AEs, nurse reviewers recorded the case details, patient information, date of the event, severity of harm, and related outcomes. A maximum of 1 hour was allotted per case. Investigators trained nurse reviewers in chart abstraction, use of the screening tool, and standardized documentation. The training included a review of a sample of cases to ensure consistency between reviewers.
We also instructed nurses to include a review of existing AE records from MSK's Surgical Secondary Events and RL6:RISQ systems. During the initial medical record review, nurse reviewers were blinded to the existing AE reports from these systems. Upon the completion of each review, the nurses were instructed to open a separate document with any AE reports. They were instructed to review the case and assess whether it met the criteria for inclusion in this study, note whether they had already identified it, and, if they had not, review the record for evidence of the event.
Event Classification
After the nurse reviewers completed the initial AE identification by reviewing the medical records and existing AE reporting databases, they presented each candidate AE to a pair of physician reviewers. The physicians had the opportunity to ask questions about the case, and nurses were encouraged to access the patient's medical record as needed. The physician reviewers were instructed by the study team to independently classify whether the event represented an AE per the study definition, the severity of harm associated with the AE, the likelihood that the event could have been prevented, and, if it had not been preventable, the likelihood that the harm resulting from the event could have been mitigated. Physicians discussed their responses and reached a consensus.
We calculated the percent agreement between physician pairs with respect to whether or not the case was an AE (98%), the level of harm severity (A-D vs E-I; 79%), the likelihood of preventability (definitely or probably preventable, definitely or probably not preventable, or unable to determine; 78%), and the likelihood of harm mitigation (definitely or probably mitigable, definitely or probably not mitigable, or unable to determine; 83%).
Analyses
We examined the total number of AEs and preventable or mitigable AEs by cancer type. We estimated the average time from the first MSK visit to the AEs. We examined AE harm severity by cancer type and preventability. We also calculated the rate of AEs and preventable or mitigable AEs per 1000 total patient days and per 100 patients and by the setting of care: AEs and preventable or mitigable AEs per 1000 inpatient days, per 1000 outpatient days, and per 100 hospital admissions.
We calculated the proportion of patients with at least 1 AE and at least 1 preventable or mitigable AE by cancer type and stage of disease. We obtained the proportion of patients with multiple AEs and the duration of time between AEs. We assessed the extent of overlap in identified AEs between data sources: medical records versus existing institutional databases (the Surgical Secondary Events and RL6:RISQ systems). The study was considered exempt research by the MSK institutional review board. All analyses were conducted with Microsoft Excel and SAS software (SAS Institute).
RESULTS
Patient Characteristics
We reviewed medical records for patients with breast (n = 128), colorectal (n = 136), and lung cancers (n = 136), who represented 133,358 total days of follow-up. The demographic, administrative, and clinical characteristics of the patient cohort are included in Table 2. The patients' mean age at the start of the study was 61 years, 32% were male, 19% were nonwhite, and 6% were Hispanic/Latino.
| Characteristic | Total | Breast Cancer | Colorectal Cancer | Lung Cancer |
|---|---|---|---|---|
| Total, No. | 400 | 128 | 136 | 136 |
| Age at start of study, mean (SD), y | 61 (13) | 55 (13) | 60 (13) | 67 (11) |
| Male, % | 32 | NA | 51 | 42 |
| Nonwhite race, % | 19 | 23 | 20 | 15 |
| Hispanic/Latino, % | 6 | 5 | 8 | 6 |
| Non-English primary language, % | 7 | 6 | 8 | 7 |
| Married (vs other), % | 66 | 62 | 68 | 68 |
| Insurance status, % | ||||
| Commercial | 55 | 70 | 56 | 40 |
| Medicare | 43 | 27 | 42 | 57 |
| Medicaid | 2 | 2 | 2 | 2 |
| Self-pay | 1 | 1 | 0 | 1 |
| No. of comorbidities, mean (SD)a | 1.2 (1.3) | 0.9 (1.0) | 1.1 (1.3) | 1.6 (1.4) |
| Early-stage disease: in situ (III) vs advanced, % | 57 | 74 | 48 | 50 |
| Cancer-directed treatment, % | ||||
| Surgery | 74 | 78 | 82 | 62 |
| Chemotherapy | 66 | 69 | 61 | 68 |
| IV chemotherapy | 86 | 72 | 98 | 90 |
| Oral chemotherapy | 37 | 49 | 30 | 33 |
| Radiation therapy | 33 | 42 | 15 | 42 |
- Abbreviations: IV, intravenous; NA, not applicable; SD, standard deviation.
- Percentages may not add up to 100% because of rounding.
- a Comorbid conditions included the following: myocardial infarction, congestive heart failure, peripheral vascular disease, cerebrovascular disease, dementia, chronic pulmonary disease, connective tissue disease, ulcer disease, mild liver disease, diabetes, hemiplegia, moderate or severe renal disease, diabetes with end organ damage, moderate or severe liver disease, AIDS, hyperlipidemia, hypertension, and coronary artery disease.
Overall AE Rates
We identified 304 unique AEs for an overall rate of 2.3 events per 1000 patient days. Ninety-seven AEs (32% of the total AEs) were deemed definitely or probably preventable. Among the 147 events deemed unpreventable, harm could have definitely or probably been mitigated for 18 (12%). The overall rate of preventable AEs was 0.73 per 1000 patient days, and the rate of mitigable AEs was 0.13 per 1000 patient days. The largest proportion of preventable or mitigable AEs of the total AEs belonged to lung cancer (47%), which was followed by colorectal cancer (36%) and breast cancer (20%; Table 3). Approximately half of all AEs occurred within 3 months of the first treatment.
| Total | Breast Cancer | Colorectal Cancer | Lung Cancer | |
|---|---|---|---|---|
| Total AEs | ||||
| No. of AEs identified | 304 | 46 | 135 | 123 |
| AEs/1000 total patient d | 2.3 | 1.0 | 2.9 | 3.0 |
| AEs/1000 total inpatient d | 91.2 | 104.6 | 84.3 | 99.7 |
| AEs/1000 total outpatient d | 0.9 | 0.5 | 0.7 | 1.4 |
| AEs/100 hospital admissions | 79.0 | 70.8 | 73.8 | 89.8 |
| Preventable or mitigable AEsa | ||||
| No. of AEs identified | 115 | 9 | 48 | 58 |
| AEs/1000 total patient d | 0.9 | 0.0 | 1.0 | 1.4 |
| AEs/1000 total inpatient d | 35.3 | 18.2 | 31.9 | 47.6 |
| AEs/1000 total outpatient d | 0.3 | 0.1 | 0.2 | 0.7 |
| AEs/100 hospital admissions | 29.9 | 13.9 | 26.2 | 42.3 |
| Preventable AEs | ||||
| No. of AEs identified | 97 | 5 | 39 | 53 |
| AEs/1000 total patient d | 0.73 | 0.11 | 0.82 | 1.30 |
| AEs/1000 total inpatient d | 31.52 | 13.64 | 27.82 | 44.48 |
| AEs/1000 total outpatient d | 0.24 | 0.04 | 0.11 | 0.60 |
| AEs/100 hospital admissions | 25.19 | 7.69 | 21.31 | 38.69 |
| Mitigable AEs | ||||
| No. of AEs identified | 18 | 4 | 9 | 5 |
| AEs/1000 total patient d | 0.13 | 0.09 | 0.19 | 0.12 |
| AEs/1000 total inpatient d | 3.82 | 4.55 | 4.09 | 3.07 |
| AEs/1000 total outpatient d | 0.08 | 0.07 | 0.09 | 0.07 |
| AEs/100 hospital admissions | 4.68 | 6.15 | 4.92 | 3.65 |
- Abbreviation: AE, adverse event.
- a These AEs were deemed probably or definitely preventable or mitigable by physician reviewers.
AEs by Setting
Sixty-three percent of AEs occurred in the inpatient setting for a rate of 79.0 AEs per 100 hospital admissions and for a rate of 29.9 preventable or mitigable AEs per 100 hospital admissions. The AE rate was 91.2 per 1000 inpatient days and 0.9 per 1000 outpatient days. For preventable AEs, the rates were 31.52 per 1000 inpatient days and 0.24 per 1000 outpatient days. For mitigable AEs, the rates were 3.82 and 0.08, respectively. The AE and preventable or mitigable AE rates by setting differed by cancer type and were mostly the highest for patients with lung cancer (Table 3).
AE Harm Severity
Physician reviewers judged 6% of overall AEs and 4% of preventable AEs to have resulted in permanent harm, to have required life-sustaining intervention, or to have resulted in death (harm categories G-I). The proportions were 13% and 0% for breast cancer, 4% and 3% for colorectal cancer, and 5% and 6% for lung cancer, respectively. Two AEs resulted in death for a rate of 0.01 events per 1000 patient days or 0.52 events per 100 hospital admissions. The overall AE rate was lower among patients with breast cancer versus patients with colorectal or lung cancers (P ≤ .001 for pairwise comparisons [Kruskal-Wallis]). However, a higher proportion of their AEs were permanent (ie, highest severity; 13% vs 4% and 5%, respectively; Table 4 and Supporting Table 1 [see online supporting information]).
| Harm Category | Total AEs, No. (% of Total) | Preventable AEs, No. (% of Total)a | Unpreventable AEs, No. (% of Total) | Unable to Determine Preventability, No. (% of Total) |
|---|---|---|---|---|
| All patients | ||||
| Total | 304 | 97 | 147 | 60 |
| Serious (D-F) | 287 (94) | 93 (96) | 137 (93) | 57 (95) |
| Permanent (G-I) | 17 (6) | 4 (4) | 10 (7) | 3 (5) |
| Breast cancer | ||||
| Total | 46 | 5 | 28 | 13 |
| Serious (D-F) | 40 (87) | 5 (100) | 23 (82) | 12 (92) |
| Permanent (G-I) | 6 (13) | 0 (0) | 5 (18) | 1 (8) |
| Colorectal cancer | ||||
| Total | 135 | 39 | 64 | 32 |
| Serious (D-F) | 130 (96) | 38 (97) | 61 (95) | 31 (97) |
| Permanent (G-I) | 5 (4) | 1 (3) | 3 (5) | 1 (3) |
| Lung cancer | ||||
| Total | 123 | 53 | 55 | 15 |
| Serious (D-F) | 117 (95) | 50 (94) | 53 (96) | 14 (93) |
| Permanent (G-I) | 6 (5) | 3 (6) | 2 (4) | 1 (7) |
- Abbreviation: AE, adverse event.
- The levels of harm were as follows: (D) the event required monitoring to confirm that it resulted in no harm to the patient and/or required intervention to preclude harm, (E) the event contributed to or resulted in temporary harm to the patient and required intervention, (F) the event contributed to or resulted in temporary harm to the patient and required initial or prolonged hospitalization, (G) the event contributed to or resulted in permanent patient harm, (H) the event contributed to or required intervention to sustain life, and (I) the event contributed to the patient's death.
- a Preventable AEs were those deemed definitely or probably preventable by physician reviewers.
Proportion of Patients With AEs
Thirty-four percent of the patients in the study cohort had at least 1 AE (95% confidence interval, 29%-39%), and 16% of the patients (95% confidence interval, 13%-20%) had a least 1 preventable or mitigable AE (Table 5). Of the patients with at least 1 AE, half had 2 or more AEs during the study period (range, 1-9). For patients with multiple events, most AEs (74%) occurred within 30 days of each other. The proportions of patients with an AE or a preventable/mitigable AE were higher among those with advanced-stage disease versus those with early-stage disease for patients with colorectal cancer (52% vs 28% for AEs and 28% vs 9% for preventable/mitigable AEs), but this was not true for patients with breast or lung cancer (Table 5).
| Cancer Type | No. of Patients | No. of Patients With at Least 1 AE (%) | No. of Patients With at Least 1 Preventable or Mitigable AE (%) |
|---|---|---|---|
| Total | 400 | 136 (34) | 64 (16) |
| Breast cancer | 128 | 27 (21) | 9 (7) |
| Early stage | 95 | 20 (21) | 8 (8) |
| Advanced stage | 33 | 7 (21) | 1 (3) |
| Colorectal cancer | 136 | 56 (41) | 26 (19) |
| Early stage | 65 | 18 (28) | 6 (9) |
| Advanced stage | 71 | 38 (54) | 20 (28) |
| Lung cancer | 136 | 53 (39) | 29 (21) |
| Early stage | 68 | 24 (35) | 14 (21) |
| Advanced stage | 68 | 29 (43) | 15 (22) |
- Abbreviation: AE, adverse event.
- For breast and colorectal cancers, stages 0 to III were considered early, and stage IV was considered advanced. For non–small cell lung cancer, stages I to III were considered early, and stage IV was considered advanced. For small cell lung cancer, the limited stage was considered early, and the extensive stage was considered advanced.
AE Types
The most frequent types of overall AEs identified were those related to infectious events (eg, Clostridium difficile; n = 50), the gastrointestinal system (eg, mucositis; n = 48), metabolic events (eg, hypokalemia; n = 47), and hematologic events (eg, thrombocytopenia; n = 37; see Table 6 and Supporting Table 2 for categorization [see online supporting information]). Examples of preventable AEs included hypomagnesemia and pressure ulcers. Of the AEs deemed preventable and/or mitigable, the permanent AEs included lymphedema for breast cancer patients, abscesses for colorectal cancer patients, and renal failure for lung cancer patients.
| AE Category | Total, No. | Breast Cancer, No. | Colorectal Cancer, No. | Lung Cancer, No. |
|---|---|---|---|---|
| Total AEs | 304 | 46 | 135 | 123 |
| Infection | 50 | 9 | 29 | 12 |
| Gastrointestinal | 48 | 9 | 30 | 9 |
| Metabolic | 47 | 2 | 12 | 33 |
| Hematologic | 37 | 3 | 17 | 17 |
| Neurologic | 24 | 6 | 9 | 9 |
| Genitourinary | 22 | 0 | 7 | 15 |
| Vascular | 22 | 2 | 12 | 8 |
| Respiratory | 13 | 1 | 6 | 6 |
| Integumentary | 11 | 10 | 1 | 0 |
| Cardiovascular | 9 | 0 | 3 | 6 |
| Other | 8 | 3 | 3 | 2 |
| Medication | 7 | 0 | 3 | 4 |
| Delay | 3 | 0 | 3 | 0 |
| Fall | 3 | 1 | 0 | 2 |
- Abbreviation: AE, adverse event.
- The categories were assigned according to clinical judgment. Examples are shown in Supporting Table 1 (see online supporting information).
AE Data Source
Most AEs were identified through medical record review only (85% or 257 AEs). Thirty-three AEs (11%) were identified in both medical records and a patient safety database. Of the overlapping AEs, most (22 AEs) were classified as harm category F and occurred during a hospitalization.
DISCUSSION
Although clinical oncologists and researchers attend routinely to treatment-related toxicities, relatively little is known about the incidence or nature of errors or treatment-related injuries that affect cancer patients in the course of treatment. In this longitudinal cohort study of 400 patients with breast, colorectal, and lung cancers, patients experienced 304 AEs. In one-third of the events, the injury was judged preventable or the severity or duration of harm could have been reduced. One in 3 patients had at least 1 AE in the year in which treatment began. Many AEs were serious and resulted in permanent harm or death. Clearly, cancer patients have a significant burden of care-related harm.
Although it is no surprise that cancer patients experience treatment-related toxicities, the extent of treatment-related harm has not been well quantified. Clinical trials frequently exclude “expected” toxicities that fall below a threshold of severity. Trials rarely report toxicities that are the result of protocol violations or deviations or usual-care lapses as preventable events. There is rarely systematic investigation of AEs affecting patients receiving routine care or per-protocol treatment outside a clinical trial or over extended periods. The literature on AEs among cancer patients in routine care is extraordinarily sparse. Most of the work in oncology focuses on medication-specific AEs.2, 15
Few studies have assessed harm across the continuum of cancer care in a longitudinal way as this study does. For outpatient events, we found the rates to be 0.9 total AEs and 0.3 preventable or mitigable AEs per 1000 outpatient days. The findings for our inpatient AEs in an oncology population (91.2 AEs and 35.3 preventable or mitigable AEs per 1000 inpatient days) are in line with published studies using the trigger tool methodology for medical record review to identify AEs in general inpatient populations. Landrigan et al24 conducted an AE assessment in 10 hospitals in North Carolina between 2002 and 2007. Investigators found 25.1 harms per 100 admissions or 56.5 harms per 1000 patient days. Sixty-three percent of the AEs were rated as preventable. Another study by Classen et al21 in 3 hospitals found higher rates: 49 events per 100 admissions and 91 events per 1000 patient days. In a pediatric inpatient population, Kirkendall et al25 found 36.7 AEs per 100 admissions and 76 AEs per 1000 patient days at a single institution. In our study focused on oncology patients, we found the inpatient AE rate per admission to be higher: 79.0 AEs per 100 hospital admissions and 29.9 preventable or mitigable AEs per 100 hospital admissions. In line with Classen et al's study, we found 91.2 events per 1000 patient days and a preventable or mitigable AE rate of 35.3 events per 1000 inpatient days. Although cancer patients are vulnerable to experiencing AEs, these findings suggest that they are in line with rates of AEs in other studied hospitalized patient populations.21, 24, 25
A single-minded focus on cancer treatment toxicities may fail to account for vulnerabilities related to other interventions (eg, adjunctive therapies and routine medications). Although expected complications may arise in surgery or radiation therapy and toxicities may be related to the properties inherent to cytotoxic or targeted therapies, the use of these therapies may not be optimized and may result in avoidable or prolonged symptoms or side effects. This study contributes to our understanding of the burden of harm in cancer care and offers a methodological contribution by using an oncology-specific trigger tool to identify AEs. This extends previous work on AE assessment in general populations to oncology settings. In light of recent interest in episode-based reimbursement models in cancer, AEs could be a major cost to providers.26, 27 In sum, our study identifies opportunities for oncology clinicians to think more broadly about oncology-related harm and the interventions that may identify, intercept, prevent, or mitigate injuries.28-30
There are some limitations. First, the study was performed at a single institution with a broad referral population and strong clinical trial program. Care of this patient population is more likely to reflect care at other comprehensive cancer centers rather than community sites. The institution's emphasis on patient safety may both increase the detection and reporting of AEs and reduce the incidence in comparison with other centers. Second, we relied on medical record review and 2 voluntary reporting systems for AE identification. Most of the existing reports were for close calls. We likely underestimated the number of AEs because medical record documentation is often incomplete.31 However, these limitations are inherent to population-based medical record studies of AEs and medical errors. Also, some AEs that occurred in the outpatient setting might have been incorrectly classified as inpatient and vice versa because of the unknown definitive causes of the AEs. Finally, the likelihood of an AE's preventability or harm mitigation was a subjective assessment by expert physician reviewers. This assessment is inherently challenging in cancer care because of the difficulty in distinguishing expected disease-related toxicities from unnecessary harm. However, we required 2 reviewers to consider the context and current best practice and to agree on each case, and they had generally good agreement.
Systematically generated information about AEs that patients experience over the course of cancer care can have several purposes. It can be used to identify issues that are common and harmful and direct interventions to prevent or mitigate the harm associated with these events. Patients and providers can be informed about what harms they might anticipate during the course of care. This information can also aid in the development of cancer-specific quality measures that can facilitate monitoring and assessing specific AEs on a regular basis.32 In future iterations, oncology signals can be automated to facilitate the identification of high-risk AEs and potential opportunities for intervening in real-time. The approach outlined and tested at our facility may be generalized to other comprehensive cancer centers and to a variety of community settings.
Our results provide insights into the nature and extent of harm, including preventable and mitigable harm, experienced by patients during cancer care over the course of 1 year at a single comprehensive cancer center. Because of ongoing developments in cancer therapies, clinicians and systems need to examine the safety of their own practices and develop approaches to address potentially preventable harm.
FUNDING SUPPORT
This study was funded by the United Hospital Fund and in part by a National Institutes of Health, Cancer Center Support Grant (P30 CA 008748). The funding sources had no role in the study.
CONFLICT OF INTEREST DISCLOSURES
David Pfister reports consulting for Boehringer Ingelheim and research funding from Boehringer Ingelheim, AstraZeneca, Exelixis, Genentech, Novartis, Merck, Lilly, GlaxoSmithKline, Bayer, and MedImmune. David Classen reports employment by and stock or other ownership in Pascal Metrics and consulting for and compensation for travel, accommodations, and expenses from Mentice, Phillips, and Health Catalyst. Aileen Killen reports employment by American International Group. Saul N. Weingart reports honoraria from UpToDate.
AUTHOR CONTRIBUTIONS
Allison Lipitz-Snyderman: Conception and design, analysis and interpretation of data, manuscript writing, critical revisions of the manuscript for important intellectual content, final approval of the manuscript, and accountability for all aspects of the work. David Pfister: Conception and design, analysis and interpretation of data, critical revisions of the manuscript for important intellectual content, final approval of the manuscript, and accountability for all aspects of the work. David Classen: Conception and design, analysis and interpretation of data, manuscript writing, critical revisions of the manuscript for important intellectual content, final approval of the manuscript, and accountability for all aspects of the work. Coral L. Atoria: Analysis and interpretation of data, critical revisions of the manuscript for important intellectual content, final approval of the manuscript, and accountability for all aspects of the work. Aileen Killen: Conception and design, analysis and interpretation of data, critical revisions of the manuscript for important intellectual content, final approval of the manuscript, and accountability for all aspects of the work. Andrew S. Epstein: Analysis and interpretation of data, critical revisions of the manuscript for important intellectual content, final approval of the manuscript, and accountability for all aspects of the work. Christopher Anderson: Analysis and interpretation of data, critical revisions of the manuscript for important intellectual content, final approval of the manuscript, and accountability for all aspects of the work. Elizabeth Fortier: Analysis and interpretation of data, critical revisions of the manuscript for important intellectual content, final approval of the manuscript, and accountability for all aspects of the work. Saul N. Weingart: Conception and design, analysis and interpretation of data, manuscript writing, critical revisions of the manuscript for important intellectual content, final approval of the manuscript, and accountability for all aspects of the work.
