Measuring potentially avoidable hospital readmissions

Journal of Clinical Epidemiology 55 (2002) 573–587

Measuring potentially avoidable hospital readmissions Patricia Halfona,*, Yves Egglib, Guy van Mellea, Julia Chevalierc, Jean-Blaise Wasserfallenc, Bernard Burnanda a

Institut Universitaire de Médecine Sociale et Préventive, University of Lausanne, Switzerland b Institut d’Économie et de Management de la Santé, University of Lausanne, Switzerland c Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland Received 13 March 2001; received in revised form 19 December 2001; accepted 19 December 2001

Abstract The objectives of this study were to develop a computerized method to screen for potentially avoidable hospital readmissions using routinely collected data and a prediction model to adjust rates for case mix. We studied hospital information system data of a random sample of 3,474 inpatients discharged alive in 1997 from a university hospital and medical records of those (1,115) readmitted within 1 year. The gold standard was set on the basis of the hospital data and medical records: all readmissions were classified as foreseen readmissions, unforeseen readmissions for a new affection, or unforeseen readmissions for a previously known affection. The latter category was submitted to a systematic medical record review to identify the main cause of readmission. Potentially avoidable readmissions were defined as a subgroup of unforeseen readmissions for a previously known affection occurring within an appropriate interval, set to maximize the chance of detecting avoidable readmissions. The computerized screening algorithm was strictly based on routine statistics: diagnosis and procedures coding and admission mode. The prediction was based on a Poisson regression model. There were 454 (13.1%) unforeseen readmissions for a previously known affection within 1 year. Fifty-nine readmissions (1.7%) were judged avoidable, most of them occurring within 1 month, which was the interval used to define potentially avoidable readmissions (n  174, 5.0%). The intra-sample sensitivity and specificity of the screening algorithm both reached approximately 96%. Higher risk for potentially avoidable readmission was associated with previous hospitalizations, high comorbidity index, and long length of stay; lower risk was associated with surgery and delivery. The model offers satisfactory predictive performance and a good medical plausibility. The proposed measure could be used as an indicator of inpatient care outcome. However, the instrument should be validated using other sets of data from various hospitals. © 2002 Elsevier Science Inc. All rights reserved. Keywords: Readmission; Avoidable; Hospitalization, Hospital quality, Risk factors

1. Introduction Many arguments justify the use of readmission rates as an indicator of hospital outcome [1–3]. First, it is a well documented fact that premature discharge or substandard care during initial hospitalization increases the risk of readmission [4–6]. Second, readmissions are frequent and involve a wide range of clinical categories, unlike hospital deaths [7]. Third, most data required to calculate and adjust readmission rates for case mix are collected routinely [2,8]. There is obviously a strong case for the use of readmission rates to assess the quality of care, from different perspectives: resource allocation [9], comparing the outcome of some surgical interventions [10], insurance contracting [11], or public information [12].

* Corresponding author. Institut Universitaire de Médecine Sociale et Préventive, Rue de Bugnon 17, CH–1005 Lausanne, Switzerland. Tel.: 4121-3147284; fax: 4121-3144954. E-mail address: [email protected] (P. Halfon).

This article proposes a rigorous measure of potentially avoidable hospital readmissions based on routinely available data. Although most studies used unplanned readmissions as a proxy of avoidable readmissions [1,13–15], there are several reasons that make this an inadequate approach. For example, even though readmissions for deliveries and organ transplants are generally unplanned, one may hardly assume that they are avoidable. Many unplanned readmissions are caused by a new affection unrelated to any diagnosis made during the previous hospitalization period. Some readmissions are justified by the treatment of a complication or by reopening a surgical site; these interventions were unforeseen at the time of the discharge of the patient but often planned subsequently during the ambulatory follow-up. Thus, a potentially avoidable readmission is necessarily unforeseen at the time of the previous discharge and related to a previously known affection. Furthermore, whereas most earlier studies have considered only readmissions occurring early after a previous discharge, no specific time interval has been justified clinically or statistically [14].

0895-4356/02/$ – see front matter © 2002 Elsevier Science Inc. All rights reserved. PII: S0895-4356(01)00 5 2 1 - 2

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A further concern is the necessity to adopt a probabilistic approach because of the usual difficulty of determining on a caseby-case basis whether a readmission might have been avoided. On the one hand, causes of readmission tend to be multiple and intricate, especially in elderly patients [16,17], and physicians often disagree about the extent to which substandard care was involved in adverse events. Investigations of the validity of the professional review process suggest that it is difficult to achieve consistent inter-rater reliability on judgments of negligence [18–20]. On the other hand, medical practice is an activity that involves some risk of adverse events, and certain incidents are acceptable if they do not occur too frequently. Because many reviews are wasted on cases with low likelihood of inadequate care, the American College of Physicians has recommended that routine case-by-case reviews should be dropped and replaced by profiles of care patterns at the institutional, regional, or national level; variations in rates of events should be monitored and used to target areas for further scrutiny [21]. The proportion of reported avoidable readmissions varies considerably from one study to the next (from 9% to 59%) [17,22–24]. A significant part of these differences is probably caused by both heterogeneity of the methods used and casemix–related factors. To set a nonbiased measure of potentially avoidable hospital readmissions, the present study addresses the following aspects sequentially: (i) setting the gold standard: identification of unforeseen readmissions for a previously known affection; (ii) definition of the optimal time interval to spot readmissions that are potentially avoidable; (iii) elaboration and validation of a computerized algorithm, exclusively based on routinely available data, to screen potentially avoidable readmissions; and (iv) development of a prediction model that makes it possible to adjust observed readmission rates for the confounding factors constituted by identifiable patient-related risks.

2. Material and methods The study was conducted in the Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland (CHUV), an 800-bed university hospital that deals with all areas of medicine except ophthalmology and psychiatry. Approximately 30% of the patients are readmitted to the CHUV during the 12 months following discharge. According to recent hospitalization reviews [25,26], inappropriate admissions are rare (admissions are justified more than 92% in surgery and neurology). Because the CHUV provides tertiary level care, few patients are readmitted to other hospitals. The population studied represented a random sample of 3,474 hospitalizations among the 21,712 hospitalizations of patients living in Canton Vaud and discharged alive between 1 January 1997 and 31 December 1997, excepting healthy newborns. The studied population was followed until 31 December 1998. The size of the sample was computed by the following formula:

S = e p(1 – p) ⁄ b 2

2

where e = 1.96 expresses a 5% risk of error; p is the potentially avoidable readmission rate estimated at 10% (based on a pre-test population); and b is the desired length of the confidence interval (CI), chosen at 0.02. A readmission was defined as an admission occurring within a specified period after the previous hospitalization (i.e., index hospitalization). Readmissions themselves were also considered as potentially exposed to another rehospitalization. The following data were extracted from the hospital information system [27]: age, gender, length of stay, admission and discharge dates, source and destination of the patients, internal transfer data, diagnosis (up to 15 ICD-10 codes) (International Statistical Classification of Diseases and Related Problems, 10th revision, 1994), interventions (up to 12 ICD-9-CM codes) (International Classification of Diseases, 9th revision, clinical modification, 1994), All-Patient Diagnosis related groups (AP-DRGs, version 12.0) [28], and planned/unplanned admission. Because financial conventions for hospitals in the canton Vaud specify that readmission within 5 days is not considered a new hospitalization, chances are that in such cases no medical diagnosis is established. Such very early readmissions were therefore systematically reviewed to ascertain that diagnoses were correctly coded and attributed to the appropriate stay. The medical records review made use of all medical and nursing notes, drug prescriptions, investigation results, clinical observations, physicians’ letters, etc. Data of each admission/readmission pair extracted from the hospital information system were printed in a standardized form and completed using medical records if necessary. They gave rise to the classification of all readmissions into the following groups by the principal investigator (gold standard): Group A—foreseen readmissions: deliveries, transplantation, chemo- and radiotherapy, treatment follow-up, rehabilitation care, planned procedure not carried out, planned surgical interventions, or other defined procedures; Group B—unforeseen readmissions caused by a new affection; and Group C—unforeseen readmissions related to a previously known affection. A readmission was considered foreseen if at the time of the previous discharge it was expected to occur as part of a program of phased care. Readmission because of a new affection means that the condition chiefly responsible could not be related to any comorbidity or disease already present during the previous stay. The classification was established by interpreting the diagnosis and procedures of the initial and subsequent stays. When the rating based on data from the hospital information system was considered obvious (e.g., chemotherapy session, delivery, elective surgery for a condition diagnosed during the previous stay, bilateral elective surgery, accidental injury), the patient’s medical record was not used. In other cases, the information was taken from the complete medical record. To ascertain the reliabil-

P. Halfon et al. / Journal of Clinical Epidemiology 55 (2002) 573–587

ity of judgment based on information system data, a random sample of 10% of admission/readmission pairs was assessed independently by three physicians. Unforeseen readmissions related to a previously known affection were considered as exposed to potentially avoidable readmissions and gave rise to a systematic medical record review specifying the main cause of readmission and assessing if the readmission could have been prevented. The principal aim at this stage was a survival analysis of time to readmission to determine the most appropriate interval between previous discharge and readmission to maximize the chance of detecting readmissions attributable to the hospital. The main cause of readmission was categorized by the principal investigator as complication following a surgical intervention, complication of another form of care, drug-related adverse event, premature discharge, discharge with a missing or erroneous diagnosis or inadequate treatment, other inadequate discharge, failed follow-up care, inadequate patient behavior, relapse or aggravation of a previously known affection, or social readmission. A priori clinical criteria were used to attribute the causes of these readmissions (see Appendix A). The 2 days preceding discharge were systematically reviewed to screen clinical instability at discharge (as defined by Kosecoff’s criteria) and thus the possibility of premature discharge [29,30]. A narrative summary of each reviewed case was entered in the database. All readmissions linked to problematic discharge (Categories d through f of Appendix A) were considered attributable to hospital care and consequently avoidable. Conversely, readmissions from Categories g through j (see Appendix A) were considered beyond the control of the hospital services, although it may be argued that certain causes are probably within partial control of the care team (e.g., readmissions resulting from patient behavior, post discharge follow-up, or recurrence of an existing disorder). Readmissions caused by complications of care (Categories a through c in Appendix A) were more difficult to categorize because of the necessity of assessing medical and nursing services for compliance with expected practice standards; these cases were classified as avoidable or not by the consensus of two experienced clinical physicians and external experts if necessary. An adverse event was judged avoidable if it resulted from the medical intervention rather than from the natural evolution of the pathological factors and if it was foreseeable and routinely preventable. For instance, we judged most wound infections and hemorrhages occurring after a simple surgical procedure avoidable, regardless of whether there was evidence of a medical error or not. An agranulocytosis following a treatment with antitumor drugs was judged avoidable in a patient with a previously known bone marrow depression and no reduction of dosage during palliative chemotherapy because this predictable adverse event might have been prevented by adapting doses, for instance. On the other hand, we considered an agranulocytosis after intensive chemotherapy for leukemia unavoidable; in this case the event is predictable, but the risk is accepted. Being neither predictable nor preventable, a drug allergy in a patient without known previous exposure to it,

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for instance, or adhesive intestinal obstructions following surgery, was considered unavoidable [31]. To estimate the degree of agreement on the cause of readmission, complete medical records of 20% of admission/readmission pairs were reviewed independently by another physician. Decision rules using only routinely available data were set up to automatically identify unforeseen readmissions related to a previously known affection. Potentially avoidable readmissions occurring within a specified interval after the previous discharge were defined as a subgroup of these. In the search for the most sensitive and specific screening algorithm, all false positives and negatives were examined to eliminate most recurrent inconsistencies. The last step involved the systematic analysis of the determinants of readmission using routinely available data and the subsequent setup of a predictive model of potentially avoidable readmission rates. 2.1. Statistical analysis The inter-rater agreement was measured by the kappa statistic [32]. The categories for setting the gold standard were unforeseen readmissions for a previously known affection and other readmissions. To estimate the goodness of fit of the principal investigator’s judgment, the proportion of sampled cases on which he disagreed with the majority opinion was also computed. The categories for setting the causes were readmissions caused by complications (Appendix A, Categories a through c), readmissions caused by problematic discharges (Appendix A, Categories d through f), and readmissions caused by other causes (Appendix A, Categories g through j). The performance of the computerized algorithm was assessed using the sensitivity and specificity of the method. The detection rate of correctly identified potentially avoidable readmissions was used as the measure of sensitivity. Specificity was measured by the proportion of readmissions correctly identified as not being potentially avoidable (foreseen readmissions and unforeseen readmissions caused by new affections). The prediction was based on a Poisson regression model, which is apt to describe the count of rare events during a specified period. This model directly estimates the risks linked to each independent variable (incidence rate ratios [IRR]). It is based on the assumption that observations are independent of each other and that risk is constant over time. The first assumption is almost met, some multiple readmissions notwithstanding. In fact, each hospitalization has its own characteristics, even if some of these have to do with the same patient; all parameters except age and gender nearly always differ significantly from one admission to the next. The second assumption was verified by measuring overdispersion, using gamma distribution to model the data and testing the null hypothesis of the alpha parameter [33]. The dependent variable corresponds to the count of potentially avoidable readmissions. Data were censored for foreseen readmissions and unforeseen readmissions caused by new affections. The remaining attrition processes, such as

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deaths occurring after index hospitalization and the patients’ possible emigration, are relatively rare over a short period. Independent exposure variables were all derived from routinely collected data relating to the index hospitalization. Age was categorized in decades and length of stay in 10-day periods; discharge location (home versus other location) and gender were considered binary variables. Diagnoses and interventions were processed using AP-DRGs classification and Charlson comorbidity score. AP-DRGs were grouped into three main clinical categories: obstetrical (delivery or abortion without surgical intervention), surgical (including intervention during delivery or abortion), and medical (including surgical intervention related to a malignant disease, AIDS or organ transplantation, and hospitalization for antenatal diagnosis) (see Appendix B). The Deyo adaptation of the Charlson comorbidity index is the most widely used approach to measuring comorbidities in administrative data [34–37]; the corresponding ICD-10 codes are listed in Appendix C. Any diagnoses present during index hospitalization were used to obtain the comorbidity index without taking data from former hospitalizations into account. The Charlson index has been expressed as a six-level variable (0, 1, 2, 3, 4, 4). The last independent exposure variable was the number of previous hospitalizations within 6 months preceding index hospitalization. A preliminary univariate analysis was conducted based on IRR. Only variables inducing IRR significantly different from 1 (P  0.05) were introduced in the multivariate model, where a forward stepwise selection procedure based on the Wald statistic was used. The contribution of each independent variable to the global model was evaluated by the log likelihood ratio. All first-order interactions were tested. The adequacy of the model was evaluated by a Pearson 2 test comparing predicted and observed values in each risk stratum. Hospitalizations with missing values were excluded from the analysis. All analyses were performed using Stata statistical software (release 6.0, Stata Corporation, College Station, TX).

3. Results 3.1. Reliability of the gold standard The kappa values by reviewer pair were 0.78, 0.81, and 0.86 (n  115). The proportion of cases for which the coinvestigators were in agreement while the principal investigator disagreed was 3% (3/103). 3.2. Description of readmissions The studied population consisted of 3,474 stays; 1,115 patients from among this population were readmitted during the 365 days following their discharge, yielding a 12-month readmission rate of 32.1%. Five readmissions (0.1%) remained unspecified because no medical records were available; 440 (12.7%) readmissions were foreseen at the time of discharge; 216 (6.2%) were unforeseen readmissions caused by a new affection; and 454 (13.1%) were unforeseen readmissions linked to a previously known affection (Table 1). As Table 2 shows, the most frequent cause of the latter category was relapse or aggravation of the initial condition. About 25% were caused by an iatrogenic complication or medical practice. The remainder was equally distributed between failure of ambulatory care and behavioral factors. The inter-rater kappa value on the causes of these readmissions was satisfactory (Kappa  0.76; n  83). Fifty-nine readmissions (1.7% of all admissions) were judged to be attributable to hospital care. The instantaneous risk of these avoidable readmissions decreases sharply over 1 month (Fig. 1) and becomes quasi-negligible after 4 months. Unforeseen readmissions linked to a previously known affection follow a similar time pattern. Unsurprisingly, readmissions for a new affection have a constant hazard function. At 1 month, the readmission rate for a previously known affection was 5.0%. Among these, 40 readmissions (1.2% of all admissions) were considered avoidable (Table 2). Finally, the usual 1-month cut-off point seems to be the optimal choice; it allows us to collect most avoidable read-

Table 1 Readmissions after index hospitalization according to the time interval between discharge and readmission. Interval (number of days) Number of casesa

0–2

2–7

7–31

31–61

61–365

Total

%

Foreseen readmissions Deliveries Transplants (organs and others) Chemotherapy or radiotherapy Treatment follow-up Rehabilitation care Planned operations not carried out Planned operations or other defined procedures Unforeseen readmissions for a new affection Unforeseen readmissions for a previously known affection Unspecified readmissions All readmissions

1 (0.0) 0 0 0 0 0 0 1 2 (0.1) 24 (0.7) 0 (0.0) 27 (0.8)

23 (0.7) 2 0 7 2 0 1 11 7 (0.3) 48 (2.1) 0 (0.0) 78 (3.0)

206 (6.6) 4 2 81 6 2 1 110 15 (0.7) 102 (5.0) 1 (0.0) 324 (12.3)

84 (9.0) 7 4 14 0 1 3 55 28 (1.5) 70 (7.0) 1 (0.1) 183 (17.6)

126 (12.7) 12 2 16 10 0 1 85 164 (6.2) 210 (13.1) 3 (0.1) 503 (32.1)

440 25 8 118 18 3 6 262 216 454 5 1115

39.5 2.2 0.7 10.6 1.6 0.3 0.5 23.5 19.4 40.7 0.4 100

a

Values in parentheses are cumulative readmission rates expressed in percent.

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Table 2 Causes of unforeseen readmissions for a previously known affection Unforeseen readmissions within 365 days Complication of surgical care Complication of nonsurgical care Drug-related adverse events Premature discharge Missing or erroneous diagnosis or inappropriate therapy Other inadequate discharge Failure of postdischarge follow-up care Inadequate patient behavior Relapse or aggravation of a previously known affection Social readmission Missing medical records Total of unforeseen readmissions for a previously known affection

missions and is sufficiently short to minimize cohort attrition (post-hospitalization death, emigration) and to nearly satisfy the Poisson assumption of constant risk over time. We therefore considered all unforeseen readmissions for a previously known affection occurring within 1 month as potentially avoidable readmissions. 3.3. Screening for potentially avoidable readmissions Fig. 2 shows the algorithm to screen potentially avoidable readmissions. Table 3 lists the criteria used to identify the three categories of readmissions. To distinguish between unforeseen readmissions for a previously known affection and those caused by a new affection, clinical judgment was used to allocate each ICD-10 code to one of 17 body-system categories (available from the authors on request). Diagnosis codes identifying a potential complication of index hospitalization are listed in Appendix D. The sensitivity and the specificity of the method applied to the 428 readmissions within 31 days both reach approximately 96% (Table 4). 3.4. Univariate analysis The monthly incidence rate of potentially avoidable readmissions did not vary significantly with age (Table 5). It was higher for men, for long stays, high comorbidity index, or when the patient had already been readmitted during the 6 months preceding index hospitalization. The risk was lower if a surgical intervention was performed during index hospitalization and negligible if the admission was caused by an obstetrical condition; it was not influenced by the discharge location.

Unforeseen readmissions within 30 days

Clearly avoidable

Total (%)

Clearly avoidable

Total (%)

31 3 4 3 9 9 — — — — — 59 (13.0)

62 (13.7) 3 (0.7) 26 (5.7) 3 (0.7) 9 (2.0) 9 (2.0) 31 (6.8) 23 (5.1) 276 (60.8) 2 (0.4) 10 (2.2) 454 (100)

16 3 1 2 9 9 — — — — — 40 (23.0)

22 (12.6) 3 (1.7) 20 (11.5) 2 (1.1) 9 (5.2) 9 (5.2) 17 (9.8) 8 (4.6) 79 (45.4) 0 (0) 5 (2.9) 174 (100)

morbidity (Charlson scores: 0, 1–2, 3, 4). The reference categories for computing the IRR were female gender, short stay, no comorbidity, medical stay, and no previous hospitalization. Gender was not retained in the final model. Three interactions were significant, all related to the “clinical category” variable. The protective effect of a surgical intervention disappeared when the Charlson score was 3: the IRR for surgical versus medical hospitalization was 0.43 (95% confidence interval [CI] 0.28–0.68) for a comorbidity score 3 but 3.03 (1.71–5.63) for higher comorbidity. The length of stay had significant impact only for surgical stays: the IRR for long stays was 1.17 (0.29–4.73) for medical hospitalizations and 5.03 (2.66–9.53) for surgical hospitalizations, unlike the “previous hospitalization” variable, which has significant impact only for medical stays. Coefficients and the IRRs of the final multivariate model are listed in Table 6. The likelihood value shows that previous hospitalization for a medical stay was the most contributory variable, followed by a Charlson comorbidity score 3, a nonmedical stay, and a long length of stay for a surgical hospitalization. The  parameter did not differ significantly from 0, suggesting that there was no overdispersion (P  0.999). The pseudo-r2 of the Poisson regression model attained 0.66. Expected and observed values (28df  4.02; P  0.85) are in good agreement, attesting to the model’s adequacy, and Table 7 shows that nearly all observed readmission rates are covered by the CI of predicted values when considering each readmission risk cell.

3.5. Multivariate analysis

4. Discussion

All significant factors (gender, length of stay, previous hospitalization, surgical intervention, obstetrical condition, and comorbidity) were included in the model as independent binary variables. Based on the results of the univariate analysis, only two categories were retained for length of stay (more or less than 40 days) and four categories for co-

Like any other indicator of quality of care, the measure of potentially avoidable readmissions should combine at least the following properties: it should be medically relevant, valid (no bias), reliable, and affordable. First, the reliability of the gold standard should be guaranteed. The high inter-rater agreement shows that in most

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Fig. 1. Kaplan Meier estimates of cumulated risk and hazard function of readmissions in each category.

cases it is simple to determine if readmissions were foreseen at the moment of discharge or unrelated to a known condition. Rare disagreements on whether or not an unforeseen readmission was related to a previously known affection arose mainly when the morbid conditions of two consecutive stays shared the same determining factors; for instance, depending on the interpretation of the role of a fall prone condition, a readmission of an old patient for a recurrent injurious fall could be considered a new condition (accidental injury) or a relapse. This information was generally found in the patient record. A high kappa value is a strong argument

but not a guarantee of a true standard that would involve a systematic chart review. More than half of the admissions/ readmission pairs (580/1,115) gave rise to a medical record review that ascertained the judgment and the adequacy of coding. The constant hazard rate of readmissions identified as caused by a new affection is consistent with an event occurring at random and argues for the validity of the classification procedure. Furthermore, other studies found similar proportions of foreseen and unforeseen readmissions; early foreseen readmissions were at least as frequent as unforeseen ones [38,39].

P. Halfon et al. / Journal of Clinical Epidemiology 55 (2002) 573–587

Fig. 2. Algorithm used to classify readmissions.

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Table 3 Criteria used to classify readmissionsa Foreseen readmissions Deliveries (1.1) Transplantations Organ transplants (1.2) Leucopherese, autologous bone marrow grafts (1.3) Chemo- and radiotherapy (1.4) Treatment follow-up (1.5) Rehabilitation (1.6) Procedure not carried out (1.7) Planned surgical interventions Material removal or replacement (1.8)

Temporary stoma closure (1.9) Postoperative aftercare (1.10) Other foreseen intervention following a surgical procedure, a delivery or an abortion during the index stay (1.11)

Other foreseen intervention following a nonsurgical index hospitalization (1.12)

Diagnostic or therapeutic nonsurgical intervention following a nonsurgical hospitalization (1.13)

Unforeseen readmissions for a new affection

Inclusion criteria

Exclusion criteria

Dr *  {060–075.9; 080–084.9; 095}

Period from index discharge to readmission 225a

Ir*  {33.5; 33.6; 37.5; 41.02–41.03; 50.51; 50.59; 52.80–52.83; 55.61; 55.69} Ir*  {41.00–41.01; 99.72; 99.73} Dr *  {Z51.0–Z51.2}

None

Dr * {Z08.0–Z08.9; Z09.0–Z09.9} Drp {Z50.0–Z50.9} Drp {Z53.0–Z53.9} Ir*  {02.07; 02.43; 02.95; 03.94; 03.97; 03.98; 37.85–37.89; 37.97–37.99; 78.60–78.69; 97.88} Ir*  {31.72; 42.83; 44.62; 46.50–46.52; 55.82; 57.82} Dr *  {Z42–Z48} Surgical readmission AP-DRG and surgical or obstetrical index AP-DRG S(Drp)  S(Di*)

A surgical readmission AP-DRG and a nonsurgical and nonobstetrical index AP-DRG S (Drp)  S(Di*) Ir*  {37.21–37.29; 42.33; 43.11; 43.41–43.42; 44.43; 45.30; 45.41–45.43; 50.11; 55.23; 88.40–88.49; 88.50–88–58; 99.62.} S(Drp)  S(Di*) S(Drp) ≠ S(Di*)

Unplanned readmission Unplanned readmission or Drp  {D69.1; D69.5; D70} Unplanned readmission Unplanned readmission Unplanned readmission Unplanned readmission or Dr *  {complication of categories 1 or 3 of Appendix C) Idem Idem Unplanned readmission or Dr *  {complication of categories 1 to 3 of Appendix C} or a reopening of surgical site I  {01.23; 03.02; 06.02; 34.03; 35.95; 39.49; 54.12; 54.61} Unplanned readmission

Unplanned readmission or procedure performed after the 2d following readmission or Dr *  {complication of categories 4 to 6 of Appendix C } Drp  {complication of categories 1 to 6 of Appendix C}

All other readmissions are unforeseen for a previously known affection Dr* any readmission diagnosis; Ir* any readmission intervention; Drp readmission main diagnosis; AP-DRG All Patients Diagnosis Related Groups; S(Drp) readmission damaged system (main diagnosis); S(Di*) Index hospitalization damaged system (any diagnosis). a The algorithm follows the paragraph sequence

The analysis of causes of unforeseen readmission for a previously known affection showed that most of them were to a certain extent under the control of hospital: a quarter of early readmissions were the consequence of hospital care; on the contrary, unavoidable readmissions for terminal care were few (only 12 at 1 month, i.e., 7%). Most early readmissions were caused by the relapse of the initial condition (main or secondary diagnosis), a cause that can be reduced by optimal care during the index stay, especially relative to the discharge process. Randomized trials have indeed demonstrated a significant reduction of early readmissions caused by various interventions (e.g., comprehensive discharge planning protocols for elderly or frail patients [40,41], nurse-directed multidisciplinary approach for patients with heart failure [42,43], symptoms management strategies in cancer patients [44], and reinforcement of communication between inpatients and outpatients settings [45]).

The reliability of the causes of readmission was lower than for the gold standard statement but sufficient to set a threshold interval to flag potentially avoidable readmissions. It must be emphasized that the kappa statistic exhibited a higher value than is usual for other adverse events [18,20,46], probably because the aim of the analysis was not to identify negligence. The proposed screening method makes use of a priori medical criteria from all areas of medicine except psychiatry. The algorithm identified first those readmissions that, with reasonable certainty, can be considered as foreseen. Any readmission for delivery was foreseeable if pregnancy was already known at the time of the preceding stay. Similarly, almost all patients readmitted for transplantation were on a waiting list, and thus readmissions were foreseen. Planned chemotherapy or radiotherapy sessions, treatment follow-up care, or rehabilitation procedures are usually foreseen. Identifying foreseen readmis-

P. Halfon et al. / Journal of Clinical Epidemiology 55 (2002) 573–587 Table 4 Sensitivity and specificity of the detection algorithma

Categories

Observed potentially avoidable readmissions (record review)

Others

Total

Predicted potentially avoidable readmissions (algorithm) Others Total

167 7 174

11 243 254

178 250 428

a

Sensitivity: 96.0%; specificity: 95.7%; total error rate 4.2%. Four hundred twenty-nine readmissions occured within 30 d; one readmission could not be categorized (unforeseen for previously known affection versus others catergories) because information system data did not give sufficient evidence and medical record was missing.

sions among planned procedures required more specific and complex criteria, which are displayed in Table 3. For example, the removal of a material must not be related to a complication to be identified as foreseen. The sequence of the algorithmic steps must be strictly followed, especially in considering as unforeseen readmissions fulfilling the exclusion criteria (Fig. 2). The systematic analysis of several hundred medical records has shown that the proposed detection method is highly sensitive and specific. However, it should be stressed that the high sensitivity and specificity are in part because of the fact that the screening algorithm uses some of the same data as the case-bycase review. These high values simply show that automatic processing of the data to a large extent reproduces the physician’s reasoning. The performance of the screening method should be measured in other settings. Furthermore, admission– readmission scenarios would be more complex and various with a time interval over 1 month, and the screening performance would be lower. It would be difficult to improve on these results; an analysis of false positive and false negative readmissions showed that they occurred only in particular situations. For example, a middle-aged man was first hospitalized for the excision of a cerebellous metastasis and readmitted 20 days later for an allergic dermatitis induced by homeopathic drugs. Because a generalized eruption caused by drugs (L27.0) is considered a iatrogenic complication, the readmission was classified as potentially avoidable by the algorithm but not by the review team (the cause of the readmission was beyond the control of the hospital). Another case was that of an old man suffering from numerous problems related to a polyarteritis nodosa (M30.0) associated with chronic viral hepatitis (B18.1). The cause of his index hospitalization was an acute attack treated by interferon, plasma exchange, and corticoid administration. Two weeks later, the patient was readmitted in emergency (unplanned) with acute pancreatitis (K85). The principal diagnosis and the index hospitalization diagnosis being related to the same system (“liver and pancreas”), the readmission was classified as potentially avoidable by the algorithm in spite of the absence of evidence to assert this. We tested the possibility of using the organ instead of the system to determine whether the affection was the same or not, but we came up with a lot of false negatives. Another example—in this case false positive—

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was the man who, during his first stay, underwent aortic valve replacement (AP-DRG 105) for stenosis to permit an elective nephrectomy for a tumor. The nephrectomy performed at readmission (AP-DRG 303) was complicated by a cardiovascular collapse (T81.1). The readmission, although actually foreseen, was thus classified as avoidable because, in the case of two subsequent surgical hospitalizations, our algorithm took into account any code for a postprocedure complication during readmission. Similar situations occurred with false negatives. An elderly woman suffering of unstable angina (I20.0) was readmitted for nausea and vomiting (R11). Because in spite of a clear causation relation the diagnostic codes were linked to different systems (cardiovascular and digestive), the readmission was considered unforeseen for a new affection. Another elderly woman was hospitalized because she fell at home, probably because she suffered from atrial fibrillation. One day later she was transferred to a rehabilitation hospital without the diagnosis of the pertrochanterian fracture she had suffered. The patient was readmitted after 2 weeks for nonsurgical treatment of the fracture. Because the index hospitalization diagnosis was missed, this readmission was misclassified as an unforeseen readmission for a new affection. All these examples show that even if the proposed algorithm is medically relevant, it is impossible to capture all situations with a priori criteria. The question of validity is solved in part by the adjusting model: a relatively high pseudo-R2 offers some evidence that the independent variables chosen contribute significantly to the model. Two factors reduce the risk of readmission: an obstetrical stay and a surgical stay with a low Charlson score. These results are consistent with common sense: Normal deliveries and surgery in patients without high comorbidity identify acute conditions that are not prone to major complications. Several studies [2,7,47] have found lower readmission rates for patients who undergo surgery. Conversely, there are three factors that increase the risk of potentially avoidable readmissions: a long term surgical stay (more than 40 days), a high Charlson score, and hospitalization within 6 months preceding a nonsurgical index hospitalization. In other words, patients who recover slowly after surgery or who suffer from many chronic comorbidities or recurrent affections run a higher risk of being readmitted. Not surprisingly, these conditions describe the deterioration of functioning status more aptly than age does, which was not retained in the model. Nevertheless, it is likely that other factors could bias the detection of potentially avoidable readmissions. Unfortunately, they are not described by routinely collected data; abnormal laboratory values, failure to ambulate, mental status, marital status, living situation, or income have all been associated with readmissions [48–50]. However, the analysis of potentially avoidable readmissions indicates that these conditions are not common (Table 2). This finding is congruent with other studies; most readmissions are caused by the relapse of the original condition and by complications linked to inappropriate inpatient care [6,17,22,23]. Demographic variables are not significantly related to risks of early readmission in

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Table 5 Univariate analysis of potentially avoidable readmissions incidence rate

All readmissions Sex Female Male Age (mean), y 0–15 (4.7) 16–35 (28.0) 36–55 (46.4) 56–75 (66.0) 76 (82.2) Length of stay (mean), d 10 (4.0) 10–20 (13.5) 20–30 (24.3) 30–40 (34.2) 40 (74.5) Charlson comorbidity score 0 1 2 3 4 Missing data Clinical category Medical Surgical Obstetrical Missing data One or more previous hospitalizations within 6 mo preceding the index stay No Yes Discharge location Home Other location

Number of readmissions

Number of person-days

Incidence rate (per 100 person-months)

174

100,622

5.27a

75 99

52,165 48,457

4.39 6.23

1 1.42 (1.04–1.94)

10 36 40 56 32

9,088 21,315 23,420 31,152 16,647

3.36 5.15 5.21 5.66 5.86

1 1.53 (0.76–3.09) 1.55 (0.78–3.10) 1.69 (0.86–3.30) 1.75 (0.86–3.55)

111 33 13 5 12

75,754 15,958 4,677 1,919 2,314

4.47 6.31 8.48 7.95 15.8

1 1.41 (0.96–2.08) 1.90 (1.06–3.33) 1.78 (0.73–4.36) 3.54 (1.95–6.48)

72 32 21 13 32 4

65,209 14,312 11,152 3,495 5,734 720

3.36 6.82 5.74 11.34 17.02

1 2.02 (1.34–3.07) 1.71 (1.05–2.77) 3.37 (1.87–6.08) 5.05 (3.33–7.66)

133 39 1 1

63,088 32,218 5,814 502

6.43 3.81 0.52

1 0.59 (0.41–0.85) 0.08 (0.01–0.58)

91 83

76,964 26,658

3.61 9.50

1 2.97 (2.17–4.04)

144 30

83,230 17,392

5.28 5.26

1 1.00 (0.67–1.54)

IRR (95% CI)

IRR  incidence rate ratio; CI  confidence interval. a The month was counted as 30.5 days: 5.27174/100,622 100 30.5

models that adequately control for patients’ clinical status [7,38,51]. Although one might assume that patients with a short length of stay, and thus more likely to have been prematurely discharged, would be at greater risk of readmission, studies did not observe such a relationship: low length

of stay outlier status was associated with reduced readmission risk in most DRG groups, whereas the opposite relationship was observed for high outliers [51]. The independent variables observed to be most consistently associated with increased readmission risk were severity-related vari-

Table 6 Multivariate analysis of readmission risk Variables retained in the model

Adjusted estimated coefficients ( )

P Value

Adjusted IRR (95% CI)

One or more previous hospitalizations within 6 mo preceding a medical index stay Length of stay 40 d and surgical stay Charlson morbidity score 3 Surgical stay and a Charlson score 3 Obstetrical stay Constant

1.076 1.991 0.792

0.510

1.96

6.706

0.001 0.001 0.001 0.035 0.050

2.93 (2.10–4.10) 7.33 (3.70–14.50) 2.21 (1.54–3.17) 0.60 (0.37–0.96) 0.14 (0.02–1.01)

IRR  incidence rate ratio; CI  confidence interval.

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Table 7 Observed and predicted values of potentially avoidable readmissions

Charlson score 3 Surgical stay Observed (expected) readmissions Observed IR per 100 person-months Expected IR per 100 person-months (95% CI) Medical stay Observed (expected) readmissions Observed IR per 100 person-months Expected IR per 100 person-months (95% CI) Charlson score 3 Surgical stay Observed (expected) readmissions Observed IR per 100 person-months Expected IR per 100 person-months (95% CI) Medical stay Observed (expected) readmissions Observed IR per 100 person-months Expected IR per 100 person-months (95% CI) Obstetrical stay Observed (expected) readmissions Observed IR per 100 person-months Expected IR per 100 person-months (95% CI)

Surgical stay with LOS 40 or medical stay without previous hospitalization

Surgical stay with LOS 40 d or medical stay with a previous hospitalization

20 (20.66) 2.17 2.24 (1.49–3.37)

6 (5.34) 18.45 16.41 (8.44–31.90)

50 (52.35) 3.56 3.73 (2.90–4.80)

47 (44.65) 11.52 10.94 (8.37–14.31)

9 (4.97) 14.92 8.24 (5.77–11.76)

4 (4.66) 51.76 60.76 (30.63–118.90)

10 (11.02) 7.47 8.24 (5.77–11.76)

22 (24.35) 21.83 24.16 (17.33–33.70) 1 (1) 0.52 0.52 (0.07–3.72)

LOS  length of stay; IR  incidence ratio, CI  confidence interval.

ables [51]. Finally, we can say that the proposed model adjusts for most potential confounding factors. However, the presence of a high potentially avoidable readmission rate should be assessed carefully because several interpretations are possible: quality of care problems or iatrogenic factors as postulated by the model, but also clinical complexity not accounted for by the model or physicians’ decisions to assume a greater risk of readmission. The current study was restricted to readmissions occurring to the same hospital. Because less than 10% of readmissions within 1 month were to other hospitals, this bias may be considered negligible. Although the use of an explicit algorithmic process guarantees the reliability of our approach, the quality of primary data is of utmost importance. Two points should be kept in mind: complications should be systematically encoded, and the principal diagnosis should reflect the reason for the admission. In consequence, potentially avoidable readmission rates should be published only if the quality of medical statistics has been assessed previously. This measure of potentially avoidable readmissions is affordable because it uses data that are routinely collected in most developed countries (AP-DRGs included). The identification algorithm is easy to write with all regular statistical packages or any database. The use of the prediction model to compare observed and predicted rates is also simple: ni Ii R = Σ i ------N Where R is the predicted cumulated incidence rate of poten-

tially avoidable readmissions; i indexes the strata (9 strata, see Table 7); ni is the number of exposure days in each risk stratum; Ii is the specific expected incidence rate at 1 month for each risk stratum; and N is the total number of exposure days. However, it should still be verified whether a simpler approach might not produce similar results. A categorization of planned/unplanned readmissions within the same interval of 31 days would have been much less sensitive (77% using the same data). This is clearly an inadequate result when we remember that 50% sensitivity is equivalent to random detection. In fact, some unforeseen readmissions requested by the treating physician are planned during follow-up for certain avoidable surgical complications, such as a wound infection arising after discharge. Another option would be to consider a single list of complications (see Appendix D, categories 1 through 6) as exclusion criteria. However, this approach results in a significant increase of the number of false positives (specificity 84%). Likewise, excluding foreseen readmissions through admission/readmission AP-DRG pairs (for example, DRG 140 angina pectoris and 112 percutaneous vascular intervention usually represent a single care episode) is unsatisfactory because the same pair may correspond both to foreseen and unforeseen readmission [3,52]. Because all simplified methods used the same primary data for less satisfactory results, we believe that the proposed algorithm is optimal. The measure we propose aims to monitor the quality of hospital care, particularly during the days preceding discharge. Our proposed threshold of 31 days was in accordance with the intuitive hypothesis that care during the previous stay does not sig-

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Appendix A Causes of unforeseen readmissions for a previously known affection Categoriesa

Description and examples

a. Complication of surgical care

Hemorrhage, disruption of wound, infection, obstruction or thrombosis of a surgical site, fistulae, pseudarthrosis, other surgical failure Postoperative medical complicationb: pulmonary embolism, decubitus ulcer, or other undesirable outcome Urinary device infection or obstruction; postlumbar puncture reaction, dialysis or catheterism complication Agranulocytosis (antitumor drug), hemorrhage (anticoagulants), others One of the following criteria (not applicable if the risk was managed that is explained in the discharge letter) New clinical problem, temperature 38.3 C, diastolic blood pressure 100 mm Hg; see Kosecoff criteria [29] for more details Abnormal blood values for potassium, sodium, creatinine, or hematocrit in the last test (during index hospitalization); see Kosecoff [29] criteria for more details Modification of a treatment, purulent drainage or purulent exudate on the day of discharge, unstable weight in patients with heart failure The cause of the readmission is a diagnosis that was ignored during index hospitalization in spite of existing symptoms or delayed or incorrect therapy Follow-up visit inappropriately scheduled in terms of patient’s severity of illness, foreseen changes in therapy or regimen, or need for laboratory evaluation Inappropriate planning of home or ambulatory health care or rehabilitation care Insufficient educational support of the patient and/or his family Includes readmissions for problems that could have been managed in an ambulatory manner The patient didn’t follow the prescribed treatment (drugs, diet, physiotherapy) or abused alcohol or illicit drugs, refused home care or his transfer to another health service, or left hospital against medical advice Recurrence, continuation, or complications of the index medical condition

b. Complication of nonsurgical care

c. Drug-related adverse event d. Premature discharge Clinical instability during last 2 d of the stay

Abnormal last laboratory value

Other criteria

e. Discharge with a missing or erroneous diagnosis or therapy

f. Other inadequate discharge

g. Failure of postdischarge follow-up care h. Inadequate patient behavior

i. Relapse or aggravation of a previously known affection j. Social readmission a b

Categories are mutually exclusive. The complication was considered as postoperative if it occurred within 4 wk after surgery [54].

Appendix B AP-DRG categories Obstetrical AP-DRG (delivery or abortion without intervention) Surgical AP-DRG (a procedure in an operating room except for an intervention for malignancy, AIDS or organ transplantation)

Surgical AP–DRG with an intervention for malignancy, AIDS or organ transplantation Medical AP–DRG

372–373, 376, 380. 1–2, 4–8, 36–42, 49–63, 75–77, 104–108, 110–120, 146–171, 191–198, 200–201, 209–213, 216–234, 261–270, 285–293, 304–315, 334–337, 339–343, 345, 353–354, 356, 360–362, 364–365, 370–371, 374–375, 377–378, 381, 392–394, 400, 415, 424, 439–443, 461, 468, 471–472, 476–479, 482–483, 491, 493–494, 530–531, 534, 536, 538–539, 545–550, 553– 556, 558–559, 564–565, 567, 571, 573, 575, 581, 583, 585, 606, 609, 610, 615–616, 622–623, 624, 650–652, 704, 730–732, 737–739, 755–759, 787, 789–793, 796–798, 806–809. 103, 199, 257–260, 302–303, 355, 356–359, 363, 401–402, 406–408, 480, 491, 557, 579, 700–703, 786, 795, 805. other AP–DRG.

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Appendix C Translation of Charlson comorbidity index components into ICD–10 codes Diagnostic category

ICD-10 codesa

Assigned weightsb

Cerebrovascular disease Congestive heart failure

I69.0–I69.4, I69.8 I11.0, I13.0, I13.2, I42.0–I42.9, I43.0–I43.2 I43.8, I50.0, I50.1, I50.9, I51.7 I27.8, I27.9, J41.0, J41.1, J41.8 J42., J43.0, J43.1, J43.2, J43.8, J43.9 J44.0, J44.1, J44.8, J44.9, J45.0, J45.1 J45.8, J45.9, J46., J47., J60., J61., J62.0 J62.8, J63.0–J63.5, J63.8, J64., J65., J66.0, J66.1, J66.2, J66.8, J68.4, J70.1, J70.3 F00.0–F00.2, F00.9, F01.0–F01.3, F01.8, F01.9, F02.0–F02.4, F02.8, F03., G30.0, G30.1, G30.8, G30.9, G31.0, G31.1 E10.0–E10.1, E10.8–E10.9, E11.0–E10.1, E11.8–E11.9, E12.0–E12.1, E12.8–E12.9, E13.0–E13.1, E13.8–E13.9, E14.0–E14.1, E14.8–E14.9, K70.3, K71.3–K71.5, K71.7–K71.8, K73.0–K73.2 K73.8, K73.9, K74.3–K74.6, K76.1, B18.0–B18.2, B18.8, B18.9 I25.2 I70.0, I70.2, I70.8–I70.9, I71.0–I71.9, I73.1, I73.9, I77.1, Z95.1, Z95.5, Z95.8–Z95.9 K25.4–K25.7, K26.4–K26.7, K27.4–K27.7, K28.4–K28.7 M05.0–M05.3, M05.8–M05.9, M06.0, M31.5, M32.0–M32.1, M32.8–M32.9, M33.2, M34.0–M34.2, M34.8–M34.9, M35.3 E10.2–E10.7, E11.2–E11.7, E12.2–E12.7, E13.2–E13.7, E14.2–E14.7 G81.0–G81.1, G81.9, G82.0–G82.5 C00.0–C41.9, C45–C76.8, C80., C81.0–C97, Z85.0–Z85.9 N18.0, N18.8–N18.9, Z49.0–Z49.2, Z99.2 I85.0, I85.9, K70.4, K71.1, K72.1, K72.9, K76.5–K76.7 B20.0–B20.9, B21.0–B21.3, B21.7–B21.9 B22.0–B22.2, B22.7 C77.0–C79.8

1 1

Chronic pulmonary disease

Dementia

Diabetes without organ damage

Mild liver disease Former myocardial infarction Peripheral vascular disease Peptic ulcer disease Rheumatic disease

Diabetes with organ damagec Hemiplegia or paraplegia Any malignancy including lymphoma or leukemia Renal disease Moderate or severe liver diseasec AIDS Metastatic solid tumorc

1

1

1

1 1 1 1 1

2 2 2 2 3 6 6

a

These codes apply to chronic processes and were therefore included in the definition of the comorbidity index if they appeared in the index hospitalization as the main or secondary diagnosis. Whereas some authors retained certain acute process codes if such existed for prior hospitalizations, the latter were not included to avoid underestimation of comorbidity in patients without prior hospitalization. b The comorbidity score is found by adding the weights of the different categories; only one illness is taken into account in each of the categories (e.g., a patient with two neoplasias has a weight of 2 and not 4). c These comorbidities take precedence over a less severe comorbidity involving the same organ (diabetes without complication, mild liver disease or non metastatic malignancy): for instance a patient with pulmonary metastases and a primary testicular tumor would have scored as 6 and not 8.

nificantly contribute to readmission if too much time has passed since the previous discharge. Indeed, most authors make use of the same interval [53]. Previous studies have similarly shown that inadequate patient care, complications, or problems with medication tend to occur earlier than readmission for unavoidable deterioration [24]. One limitation of the approach is that all readmission problems are weighted equally, independently of the degree to which they can be avoided. The rate and the causes of unforeseen readmissions in the study setting were quite similar to those in other studies [17,22– 24]. Contradicting a widespread belief, readmissions attributable to hospital care were rather low, representing 1.2% of admissions, a rate much like the 1% reported by Frankl et al. [22]. Finally, the proposed instrument is medically relevant, sen-

sitive, specific, and takes into account most potential confounding factors. The completeness and reliability of medical coding is an important prerequisite for satisfactory use of this tool. The present analysis should nonetheless be extended to other hospitals to gather more information about variations of potentially avoidable readmissions. Comparative studies are required before setting a benchmark determining an acceptable level of potentially avoidable readmission.

Acknowledgments We thank the referees for their constructive comments that substantially improved this paper.

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Appendix D List of ICD-10 codes of complications Type of complication

ICD-10 codes

1. Related to surgical care

E89.0–E89.9, H59.0–H59.9, G97.0–G97.2, G97.8–G97.9, H95.0–H95.1, H95.8–H95.9, I97.0–I97.2, I97.8–I97.9, J95.0–J95.9, K43.0–K43.9, K91.0–K91.5, K91.8–K91.9, M02.0, M80.3, M81.3, M84.0, M84.1, M96.0–M96.1, M96.3–M96.4, M96.6, M96.8–M96.9, N99.0–N99.5, N99.8–N99.9, O35.7, T81.0–T81.9, T82.0–T82.9, T83.0–T83.9, T84.0–T84.9, T85.0–T85.9, T86.0–T86.4, T86.8–T86.9, T87.0–T87.6. O04.0–O04.8, O05.0–O05.8, O06.0–O06.8, O07.0–O07.3, O08.0–O08.9, O85, O86.0–O86.4, O86.8, O87.0–O87.3, O87.8–O87.9, O88.0–O88.3, O88.8, O90.0–O90.9, O91.0–O91.2, O92.0–O92.7, O96, O97. J85.0–J85.3, J86.0, J86.9, M00.0–M00.9, M46.2, M46.3, M86.2–M86.4, M86.6–M86.9, T79.3. D61.1, D69.5, D70, E06.4, E13.–E13.9, E16.0, E23.1, E24.2, E27.3, E66.1, G21.0, G21.1,G25.1, G24.0, G25.4; G25.6, G44.4, G62.0, H26.3, H40.6, I95.2, K52.0, K62.7, K71.0–K71.9, L23.3, L24.4, L27.0–L27.1, L51.2, L53.0, L56.0–L56.1, L58.0–L58.1, L58.9, L59.0, L59.8, L59.9, M02.2, M10.2, M32.0, M80.4, M81.4, M83.5, M87.1, M96.5, N14.0–N14.2, N30.4, N98.0–N98.3, N98.8, N98.9, O29.0–O29.6, O29.8–O29.9, O35.5–O35.6, O89.0–O89.6, O89.8–O89.9, T80.0, T80.1–T80.6, T80.8–T80.9, T88.0–T88.9. B20.0–B20.9, B21.0–B21.3, B21.7–B21.9, B22.0–B22.2, B22.7, B25.0–B25.2, B25.8–B25.9, C77.0–C77.9, C78.0–C78.8, C79.0–C79.8, C80., I46.0–I46.9, I85.0, J81, R40.0–R40.2, R55, R57.0–R57.1, R57.8–R57.9 I26.0, I26.9, I80.0–I80.3, I80.8–I80.9, I81, I82.0–I82.3, I82.8–I82.9, L89

2. Related to a delivery or an abortion

3. Some infections of a surgical site classified elsewhere 4. Drug- or radiation-induced disorders

5. Conditions generally resulting from a preexisting disease with multiple accompanying diseases 6. Deep vein thrombosis, pulmonary embolism, and decubitus ulcer

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